Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i7.25962
Liangyu Huo, Zulin Wang, Mai Xu
Imitation learning (IL) has recently shown impressive performance in training a reinforcement learning agent with human demonstrations, eliminating the difficulty of designing elaborate reward functions in complex environments. However, most IL methods work under the assumption of the optimality of the demonstrations and thus cannot learn policies to surpass the demonstrators. Some methods have been investigated to obtain better-than-demonstration (BD) performance with inner human feedback or preference labels. In this paper, we propose a method to learn rewards from suboptimal demonstrations via a weighted preference learning technique (LERP). Specifically, we first formulate the suboptimality of demonstrations as the inaccurate estimation of rewards. The inaccuracy is modeled with a reward noise random variable following the Gumbel distribution. Moreover, we derive an upper bound of the expected return with different noise coefficients and propose a theorem to surpass the demonstrations. Unlike existing literature, our analysis does not depend on the linear reward constraint. Consequently, we develop a BD model with a weighted preference learning technique. Experimental results on continuous control and high-dimensional discrete control tasks show the superiority of our LERP method over other state-of-the-art BD methods.
{"title":"Learning Noise-Induced Reward Functions for Surpassing Demonstrations in Imitation Learning","authors":"Liangyu Huo, Zulin Wang, Mai Xu","doi":"10.1609/aaai.v37i7.25962","DOIUrl":"https://doi.org/10.1609/aaai.v37i7.25962","url":null,"abstract":"Imitation learning (IL) has recently shown impressive performance in training a reinforcement learning agent with human demonstrations, eliminating the difficulty of designing elaborate reward functions in complex environments. However, most IL methods work under the assumption of the optimality of the demonstrations and thus cannot learn policies to surpass the demonstrators. Some methods have been investigated to obtain better-than-demonstration (BD) performance with inner human feedback or preference labels. In this paper, we propose a method to learn rewards from suboptimal demonstrations via a weighted preference learning technique (LERP). Specifically, we first formulate the suboptimality of demonstrations as the inaccurate estimation of rewards. The inaccuracy is modeled with a reward noise random variable following the Gumbel distribution. Moreover, we derive an upper bound of the expected return with different noise coefficients and propose a theorem to surpass the demonstrations. Unlike existing literature, our analysis does not depend on the linear reward constraint. Consequently, we develop a BD model with a weighted preference learning technique. Experimental results on continuous control and high-dimensional discrete control tasks show the superiority of our LERP method over other state-of-the-art BD methods.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"50 1","pages":"7953-7961"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87259615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Verifying the facts alleged by the prosecutors before the trial requires the judges to retrieve evidence within the massive materials accompanied. Existing Legal AI applications often assume the facts are already determined and fail to notice the difficulty of reconstructing them. To build a practical Legal AI application and free the judges from the manually searching work, we introduce the task of Legal Evidence Retrieval, which aims at automatically retrieving the precise fact-related verbal evidence within a single case. We formulate the task in a dense retrieval paradigm, and jointly learn the constrastive representations and alignments between facts and evidence. To get rid of the tedious annotations, we construct an approximated positive vector for a given fact by aggregating a set of evidence from the same case. An entropy-based denoise technique is further applied to mitigate the impact of false positive samples. We train our models on tens of thousands of unlabeled cases and evaluate them on a labeled dataset containing 919 cases and 4,336 queries. Experimental results indicate that our approach is effective and outperforms other state-of-the-art representation and retrieval models. The dataset and code are available at https://github.com/yaof20/LER.
{"title":"Unsupervised Legal Evidence Retrieval via Contrastive Learning with Approximate Aggregated Positive","authors":"Feng Yao, Jingyuan Zhang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Yun Liu, Weixing Shen","doi":"10.1609/aaai.v37i4.25603","DOIUrl":"https://doi.org/10.1609/aaai.v37i4.25603","url":null,"abstract":"Verifying the facts alleged by the prosecutors before the trial requires the judges to retrieve evidence within the massive materials accompanied.\u0000Existing Legal AI applications often assume the facts are already determined and fail to notice the difficulty of reconstructing them. To build a practical Legal AI application and free the judges from the manually searching work, we introduce the task of Legal Evidence Retrieval, which aims at automatically retrieving the precise fact-related verbal evidence within a single case. We formulate the task in a dense retrieval paradigm, and jointly learn the constrastive representations and alignments between facts and evidence. To get rid of the tedious annotations, we construct an approximated positive vector for a given fact by aggregating a set of evidence from the same case. An entropy-based denoise technique is further applied to mitigate the impact of false positive samples. We train our models on tens of thousands of unlabeled cases and evaluate them on a labeled dataset containing 919 cases and 4,336 queries. Experimental results indicate that our approach is effective and outperforms other state-of-the-art representation and retrieval models. The dataset and code are available at https://github.com/yaof20/LER.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"21 1","pages":"4783-4791"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87256005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i9.26340
Shuping Zhao, Jie Wen, Lunke Fei, Bob Zhang
Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. Firstly, owing to the effectiveness of the low-rank representation in revealing the inherent structure of the data, we exploit it to infer the missing instances and construct the complete graph for each view. Afterwards, inspired by the structural consistency, a between-view consistency constraint is imposed to guarantee the similarity of the graphs from different views. More importantly, the TIMVC_IGC simultaneously learns the low-rank structures of the different views and explores the correlations of the different graphs in a latent manifold sub-space using a low-rank tensor constraint, such that the intrinsic graphs of the different views can be obtained. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. Experimental results on several real-world databases illustrates that the proposed method can outperform the other state-of-the-art related methods for incomplete multi-view clustering.
{"title":"Tensorized Incomplete Multi-View Clustering with Intrinsic Graph Completion","authors":"Shuping Zhao, Jie Wen, Lunke Fei, Bob Zhang","doi":"10.1609/aaai.v37i9.26340","DOIUrl":"https://doi.org/10.1609/aaai.v37i9.26340","url":null,"abstract":"Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. Firstly, owing to the effectiveness of the low-rank representation in revealing the inherent structure of the data, we exploit it to infer the missing instances and construct the complete graph for each view. Afterwards, inspired by the structural consistency, a between-view consistency constraint is imposed to guarantee the similarity of the graphs from different views. More importantly, the TIMVC_IGC simultaneously learns the low-rank structures of the different views and explores the correlations of the different graphs in a latent manifold sub-space using a low-rank tensor constraint, such that the intrinsic graphs of the different views can be obtained. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. Experimental results on several real-world databases illustrates that the proposed method can outperform the other state-of-the-art related methods for incomplete multi-view clustering.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"24 1","pages":"11327-11335"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87345086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i9.26355
Lin Zhu, Xinbing Wang, Cheng Zhou, Nanyang Ye
Recent advances in large pre-trained models showed promising results in few-shot learning. However, their generalization ability on two-dimensional Out-of-Distribution (OoD) data, i.e., correlation shift and diversity shift, has not been thoroughly investigated. Researches have shown that even with a significant amount of training data, few methods can achieve better performance than the standard empirical risk minimization method (ERM) in OoD generalization. This few-shot OoD generalization dilemma emerges as a challenging direction in deep neural network generalization research, where the performance suffers from overfitting on few-shot examples and OoD generalization errors. In this paper, leveraging a broader supervision source, we explore a novel Bayesian cross-modal image-text alignment learning method (Bayes-CAL) to address this issue. Specifically, the model is designed as only text representations are fine-tuned via a Bayesian modelling approach with gradient orthogonalization loss and invariant risk minimization (IRM) loss. The Bayesian approach is essentially introduced to avoid overfitting the base classes observed during training and improve generalization to broader unseen classes. The dedicated loss is introduced to achieve better image-text alignment by disentangling the causal and non-casual parts of image features. Numerical experiments demonstrate that Bayes-CAL achieved state-of-the-art OoD generalization performances on two-dimensional distribution shifts. Moreover, compared with CLIP-like models, Bayes-CAL yields more stable generalization performances on unseen classes. Our code is available at https://github.com/LinLLLL/BayesCAL.
{"title":"Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization","authors":"Lin Zhu, Xinbing Wang, Cheng Zhou, Nanyang Ye","doi":"10.1609/aaai.v37i9.26355","DOIUrl":"https://doi.org/10.1609/aaai.v37i9.26355","url":null,"abstract":"Recent advances in large pre-trained models showed promising results in few-shot learning. However, their generalization ability on two-dimensional Out-of-Distribution (OoD) data, i.e., correlation shift and diversity shift, has not been thoroughly investigated. Researches have shown that even with a significant amount of training data, few methods can achieve better performance than the standard empirical risk minimization method (ERM) in OoD generalization. This few-shot OoD generalization dilemma emerges as a challenging direction in deep neural network generalization research, where the performance suffers from overfitting on few-shot examples and OoD generalization errors. In this paper, leveraging a broader supervision source, we explore a novel Bayesian cross-modal image-text alignment learning method (Bayes-CAL) to address this issue. Specifically, the model is designed as only text representations are fine-tuned via a Bayesian modelling approach with gradient orthogonalization loss and invariant risk minimization (IRM) loss. The Bayesian approach is essentially introduced to avoid overfitting the base classes observed during training and improve generalization to broader unseen classes. The dedicated loss is introduced to achieve better image-text alignment by disentangling the causal and non-casual parts of image features. Numerical experiments demonstrate that Bayes-CAL achieved state-of-the-art OoD generalization performances on two-dimensional distribution shifts. Moreover, compared with CLIP-like models, Bayes-CAL yields more stable generalization performances on unseen classes. Our code is available at https://github.com/LinLLLL/BayesCAL.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"26 1","pages":"11461-11469"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80659733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i9.26276
Fu-qiang Yang, Xin Li, Min Wang, Hongyu Zang, W. Pang, Mingzhong Wang
Multivariate time series (MTS) analysis and forecasting are crucial in many real-world applications, such as smart traffic management and weather forecasting. However, most existing work either focuses on short sequence forecasting or makes predictions predominantly with time domain features, which is not effective at removing noises with irregular frequencies in MTS. Therefore, we propose WaveForM, an end-to-end graph enhanced Wavelet learning framework for long sequence FORecasting of MTS. WaveForM first utilizes Discrete Wavelet Transform (DWT) to represent MTS in the wavelet domain, which captures both frequency and time domain features with a sound theoretical basis. To enable the effective learning in the wavelet domain, we further propose a graph constructor, which learns a global graph to represent the relationships between MTS variables, and graph-enhanced prediction modules, which utilize dilated convolution and graph convolution to capture the correlations between time series and predict the wavelet coefficients at different levels. Extensive experiments on five real-world forecasting datasets show that our model can achieve considerable performance improvement over different prediction lengths against the most competitive baseline of each dataset.
{"title":"WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series","authors":"Fu-qiang Yang, Xin Li, Min Wang, Hongyu Zang, W. Pang, Mingzhong Wang","doi":"10.1609/aaai.v37i9.26276","DOIUrl":"https://doi.org/10.1609/aaai.v37i9.26276","url":null,"abstract":"Multivariate time series (MTS) analysis and forecasting are crucial in many real-world applications, such as smart traffic management and weather forecasting. However, most existing work either focuses on short sequence forecasting or makes predictions predominantly with time domain features, which is not effective at removing noises with irregular frequencies in MTS. Therefore, we propose WaveForM, an end-to-end graph enhanced Wavelet learning framework for long sequence FORecasting of MTS. WaveForM first utilizes Discrete Wavelet Transform (DWT) to represent MTS in the wavelet domain, which captures both frequency and time domain features with a sound theoretical basis. To enable the effective learning in the wavelet domain, we further propose a graph constructor, which learns a global graph to represent the relationships between MTS variables, and graph-enhanced prediction modules, which utilize dilated convolution and graph convolution to capture the correlations between time series and predict the wavelet coefficients at different levels. Extensive experiments on five real-world forecasting datasets show that our model can achieve considerable performance improvement over different prediction lengths against the most competitive baseline of each dataset.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"9 1","pages":"10754-10761"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85171922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i4.25594
Xiaobao Wang, Yiqi Dong, Di Jin, Yawen Li, Longbiao Wang, J. Dang
Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches for drawing long-range incongruity patterns. However, the manually constructed graph structure might be prone to errors (e.g., noisy or incomplete) and not optimal for the sarcasm detection task. Errors produced during the graph construction step cannot be remedied and may accrue to the following stages, resulting in poor performance. To surmount the above limitations, we explore a novel Iterative Augmenting Affective Graph and Dependency Graph (IAAD) framework to jointly and iteratively learn the incongruity graph structure. IAAD can alternatively update the incongruity graph structure and node representation until the learning graph structure is optimal for the metrics of sarcasm detection. More concretely, we begin with deriving an affective and a dependency graph for each instance, then an iterative incongruity graph learning module is employed to augment affective and dependency graphs for obtaining the optimal inconsistent semantic graph with the goal of optimizing the graph for the sarcasm detection task. Extensive experiments on three datasets demonstrate that the proposed model outperforms state-of-the-art baselines for sarcasm detection with significant margins.
{"title":"Augmenting Affective Dependency Graph via Iterative Incongruity Graph Learning for Sarcasm Detection","authors":"Xiaobao Wang, Yiqi Dong, Di Jin, Yawen Li, Longbiao Wang, J. Dang","doi":"10.1609/aaai.v37i4.25594","DOIUrl":"https://doi.org/10.1609/aaai.v37i4.25594","url":null,"abstract":"Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches for drawing long-range incongruity patterns. However, the manually constructed graph structure might be prone to errors (e.g., noisy or incomplete) and not optimal for the sarcasm detection task. Errors produced during the graph construction step cannot be remedied and may accrue to the following stages, resulting in poor performance. To surmount the above limitations, we explore a novel Iterative Augmenting Affective Graph and Dependency Graph (IAAD) framework to jointly and iteratively learn the incongruity graph structure. IAAD can alternatively update the incongruity graph structure and node representation until the learning graph structure is optimal for the metrics of sarcasm detection. More concretely, we begin with deriving an affective and a dependency graph for each instance, then an iterative incongruity graph learning module is employed to augment affective and dependency graphs for obtaining the optimal inconsistent semantic graph with the goal of optimizing the graph for the sarcasm detection task. Extensive experiments on three datasets demonstrate that the proposed model outperforms state-of-the-art baselines for sarcasm detection with significant margins.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"31 1","pages":"4702-4710"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86997075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i13.27068
Francesco Fuggitti, T. Chakraborti
This is a demonstration of our newly released Python package NL2LTL which leverages the latest in natural language understanding (NLU) and large language models (LLMs) to translate natural language instructions to linear temporal logic (LTL) formulas. This allows direct translation to formal languages that a reasoning system can use, while at the same time, allowing the end-user to provide inputs in natural language without having to understand any details of an underlying formal language. The package comes with support for a set of default LTL patterns, corresponding to popular DECLARE templates, but is also fully extensible to new formulas and user inputs. The package is open-source and is free to use for the AI community under the MIT license. Open Source: https://github.com/IBM/nl2ltl. Video Link: https://bit.ly/3dHW5b1
{"title":"NL2LTL - a Python Package for Converting Natural Language (NL) Instructions to Linear Temporal Logic (LTL) Formulas","authors":"Francesco Fuggitti, T. Chakraborti","doi":"10.1609/aaai.v37i13.27068","DOIUrl":"https://doi.org/10.1609/aaai.v37i13.27068","url":null,"abstract":"This is a demonstration of our newly released Python package NL2LTL which leverages the latest in natural language understanding (NLU) and large language models (LLMs) to translate natural language instructions to linear temporal logic (LTL) formulas. This allows direct translation to formal languages that a reasoning system can use, while at the same time, allowing the end-user to provide inputs in natural language without having to understand any details of an underlying \u0000formal language. The package comes with support for a set of default LTL patterns, corresponding to popular DECLARE templates, but is also fully extensible to new formulas and user inputs. The package is open-source and is free to use for the AI community under the MIT license. Open Source: https://github.com/IBM/nl2ltl. Video Link: https://bit.ly/3dHW5b1","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"21 1","pages":"16428-16430"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90754750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i11.26528
Jaeseong Lee, Dohyeon Lee, Seung-won Hwang
Although multilingual pretrained models (mPLMs) enabled support of various natural language processing in diverse languages, its limited coverage of 100+ languages lets 6500+ languages remain ‘unseen’. One common approach for an unseen language is specializing the model for it as target, by performing additional masked language modeling (MLM) with the target language corpus. However, we argue that, due to the discrepancy from multilingual MLM pretraining, a naive specialization as such can be suboptimal. Specifically, we pose three discrepancies to overcome. Script and linguistic discrepancy of the target language from the related seen languages, hinder a positive transfer, for which we propose to maximize representation similarity, unlike existing approaches maximizing overlaps. In addition, label space for MLM prediction can vary across languages, for which we propose to reinitialize top layers for a more effective adaptation. Experiments over four different language families and three tasks shows that our method improves the task performance of unseen languages with statistical significance, while previous approach fails to.
{"title":"Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization","authors":"Jaeseong Lee, Dohyeon Lee, Seung-won Hwang","doi":"10.1609/aaai.v37i11.26528","DOIUrl":"https://doi.org/10.1609/aaai.v37i11.26528","url":null,"abstract":"Although multilingual pretrained models (mPLMs) enabled support of various natural language processing in diverse languages, its limited coverage of 100+ languages lets 6500+ languages remain ‘unseen’. One common approach for an unseen language is specializing the model for it as target, by performing additional masked language modeling (MLM) with the target language corpus. However, we argue that, due to the discrepancy from multilingual MLM pretraining, a naive specialization as such can be suboptimal. Specifically, we pose three discrepancies to overcome. Script and linguistic discrepancy of the target language from the related seen languages, hinder a positive transfer, for which we propose to maximize representation similarity, unlike existing approaches maximizing overlaps. In addition, label space for MLM prediction can vary across languages, for which we propose to reinitialize top layers for a more effective adaptation. Experiments over four different language families and three tasks shows that our method improves the task performance of unseen languages with statistical significance, while previous approach fails to.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"33 1","pages":"13004-13013"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91254954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i11.26609
Lianwei Wu, Pusheng Liu, Yanning Zhang
The existing approaches based on different neural networks automatically capture and fuse the multimodal semantics of news, which have achieved great success for fake news detection. However, they still suffer from the limitations of both shallow fusion of multimodal features and less attention to the inconsistency between different modalities. To overcome them, we propose multi-reading habits fusion reasoning networks (MRHFR) for multi-modal fake news detection. In MRHFR, inspired by people's different reading habits for multimodal news, we summarize three basic cognitive reading habits and put forward cognition-aware fusion layer to learn the dependencies between multimodal features of news, so as to deepen their semantic-level integration. To explore the inconsistency of different modalities of news, we develop coherence constraint reasoning layer from two perspectives, which first measures the semantic consistency between the comments and different modal features of the news, and then probes the semantic deviation caused by unimodal features to the multimodal news content through constraint strategy. Experiments on two public datasets not only demonstrate that MRHFR not only achieves the excellent performance but also provides a new paradigm for capturing inconsistencies between multi-modal news.
{"title":"See How You Read? Multi-Reading Habits Fusion Reasoning for Multi-Modal Fake News Detection","authors":"Lianwei Wu, Pusheng Liu, Yanning Zhang","doi":"10.1609/aaai.v37i11.26609","DOIUrl":"https://doi.org/10.1609/aaai.v37i11.26609","url":null,"abstract":"The existing approaches based on different neural networks automatically capture and fuse the multimodal semantics of news, which have achieved great success for fake news detection. However, they still suffer from the limitations of both shallow fusion of multimodal features and less attention to the inconsistency between different modalities. To overcome them, we propose multi-reading habits fusion reasoning networks (MRHFR) for multi-modal fake news detection. In MRHFR, inspired by people's different reading habits for multimodal news, we summarize three basic cognitive reading habits and put forward cognition-aware fusion layer to learn the dependencies between multimodal features of news, so as to deepen their semantic-level integration. To explore the inconsistency of different modalities of news, we develop coherence constraint reasoning layer from two perspectives, which first measures the semantic consistency between the comments and different modal features of the news, and then probes the semantic deviation caused by unimodal features to the multimodal news content through constraint strategy. Experiments on two public datasets not only demonstrate that MRHFR not only achieves the excellent performance but also provides a new paradigm for capturing inconsistencies between multi-modal news.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"10 1","pages":"13736-13744"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89942549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1609/aaai.v37i2.25321
J. Shim, Hyunwoo Yu, Kyeongbo Kong, Suk-Ju Kang
With the success of Vision Transformer (ViT) in image classification, its variants have yielded great success in many downstream vision tasks. Among those, the semantic segmentation task has also benefited greatly from the advance of ViT variants. However, most studies of the transformer for semantic segmentation only focus on designing efficient transformer encoders, rarely giving attention to designing the decoder. Several studies make attempts in using the transformer decoder as the segmentation decoder with class-wise learnable query. Instead, we aim to directly use the encoder features as the queries. This paper proposes the Feature Enhancing Decoder transFormer (FeedFormer) that enhances structural information using the transformer decoder. Our goal is to decode the high-level encoder features using the lowest-level encoder feature. We do this by formulating high-level features as queries, and the lowest-level feature as the key and value. This enhances the high-level features by collecting the structural information from the lowest-level feature. Additionally, we use a simple reformation trick of pushing the encoder blocks to take the place of the existing self-attention module of the decoder to improve efficiency. We show the superiority of our decoder with various light-weight transformer-based decoders on popular semantic segmentation datasets. Despite the minute computation, our model has achieved state-of-the-art performance in the performance computation trade-off. Our model FeedFormer-B0 surpasses SegFormer-B0 with 1.8% higher mIoU and 7.1% less computation on ADE20K, and 1.7% higher mIoU and 14.4% less computation on Cityscapes, respectively. Code will be released at: https://github.com/jhshim1995/FeedFormer.
{"title":"FeedFormer: Revisiting Transformer Decoder for Efficient Semantic Segmentation","authors":"J. Shim, Hyunwoo Yu, Kyeongbo Kong, Suk-Ju Kang","doi":"10.1609/aaai.v37i2.25321","DOIUrl":"https://doi.org/10.1609/aaai.v37i2.25321","url":null,"abstract":"With the success of Vision Transformer (ViT) in image classification, its variants have yielded great success in many downstream vision tasks. Among those, the semantic segmentation task has also benefited greatly from the advance of ViT variants. However, most studies of the transformer for semantic segmentation only focus on designing efficient transformer encoders, rarely giving attention to designing the decoder. Several studies make attempts in using the transformer decoder as the segmentation decoder with class-wise learnable query. Instead, we aim to directly use the encoder features as the queries. This paper proposes the Feature Enhancing Decoder transFormer (FeedFormer) that enhances structural information using the transformer decoder. Our goal is to decode the high-level encoder features using the lowest-level encoder feature. We do this by formulating high-level features as queries, and the lowest-level feature as the key and value. This enhances the high-level features by collecting the structural information from the lowest-level feature. Additionally, we use a simple reformation trick of pushing the encoder blocks to take the place of the existing self-attention module of the decoder to improve efficiency. We show the superiority of our decoder with various light-weight transformer-based decoders on popular semantic segmentation datasets. Despite the minute computation, our model has achieved state-of-the-art performance in the performance computation trade-off. Our model FeedFormer-B0 surpasses SegFormer-B0 with 1.8% higher mIoU and 7.1% less computation on ADE20K, and 1.7% higher mIoU and 14.4% less computation on Cityscapes, respectively. Code will be released at: https://github.com/jhshim1995/FeedFormer.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"5 1","pages":"2263-2271"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90036945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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