Pub Date : 2022-10-17DOI: 10.1017/S1351324922000444
S. Yagi, A. Elnagar, Shehdeh Fareh
Abstract Modelling the distributional semantics of such a morphologically rich language as Arabic needs to take into account its introflexive, fusional, and inflectional nature attributes that make up its combinatorial sequences and substitutional paradigms. To evaluate such word distributional models, the benchmarks that have been used thus far in Arabic have mimicked those in English. This paper reports on a benchmark that we designed to reflect linguistic patterns in both Contemporary Arabic and Classical Arabic, the first being a cover term for written and spoken Modern Standard Arabic, while the second for pre-modern Arabic. The analogy items we included in this benchmark are chosen in a transparent manner such that they would capture the major features of nouns and verbs; derivational and inflectional morphology; high-, middle-, and low-frequency patterns and lexical items; and morphosemantic, morphosyntactic, and semantic dimensions of the language. All categories included in this benchmark are carefully selected to ensure proper representation of the language. The benchmark consists of 45 roots of the trilateral, all-consonantal, and semivowel-inclusive types; six morphosemantic patterns (’af‘ala; ifta‘ala; infa‘ala; istaf‘ala; tafa‘‘ala; and tafā‘ala); five derivations (the verbal noun, active participle, and the contrasts in Masculine-Feminine; Feminine-Singular-Plural; Masculine-Singular-Plural); and morphosyntactic transformations (perfect and imperfect verbs conjugated for all pronouns); and lexical semantics (synonyms, antonyms, and hyponyms of nouns, verbs, and adjectives), as well as capital cities and currencies. All categories include an equal proportion of high-, medium-, and low-frequency items. For the purpose of validating the proposed benchmark, we developed a set of embedding models from different textual sources. Then, we tested them intrinsically using the proposed benchmark and extrinsically using two natural language processing tasks: Arabic Named Entity Recognition and Text Classification. The evaluation leads to the conclusion that the proposed benchmark is truly reflective of this morphologically rich language and discriminatory of word embeddings.
{"title":"A benchmark for evaluating Arabic word embedding models","authors":"S. Yagi, A. Elnagar, Shehdeh Fareh","doi":"10.1017/S1351324922000444","DOIUrl":"https://doi.org/10.1017/S1351324922000444","url":null,"abstract":"Abstract Modelling the distributional semantics of such a morphologically rich language as Arabic needs to take into account its introflexive, fusional, and inflectional nature attributes that make up its combinatorial sequences and substitutional paradigms. To evaluate such word distributional models, the benchmarks that have been used thus far in Arabic have mimicked those in English. This paper reports on a benchmark that we designed to reflect linguistic patterns in both Contemporary Arabic and Classical Arabic, the first being a cover term for written and spoken Modern Standard Arabic, while the second for pre-modern Arabic. The analogy items we included in this benchmark are chosen in a transparent manner such that they would capture the major features of nouns and verbs; derivational and inflectional morphology; high-, middle-, and low-frequency patterns and lexical items; and morphosemantic, morphosyntactic, and semantic dimensions of the language. All categories included in this benchmark are carefully selected to ensure proper representation of the language. The benchmark consists of 45 roots of the trilateral, all-consonantal, and semivowel-inclusive types; six morphosemantic patterns (’af‘ala; ifta‘ala; infa‘ala; istaf‘ala; tafa‘‘ala; and tafā‘ala); five derivations (the verbal noun, active participle, and the contrasts in Masculine-Feminine; Feminine-Singular-Plural; Masculine-Singular-Plural); and morphosyntactic transformations (perfect and imperfect verbs conjugated for all pronouns); and lexical semantics (synonyms, antonyms, and hyponyms of nouns, verbs, and adjectives), as well as capital cities and currencies. All categories include an equal proportion of high-, medium-, and low-frequency items. For the purpose of validating the proposed benchmark, we developed a set of embedding models from different textual sources. Then, we tested them intrinsically using the proposed benchmark and extrinsically using two natural language processing tasks: Arabic Named Entity Recognition and Text Classification. The evaluation leads to the conclusion that the proposed benchmark is truly reflective of this morphologically rich language and discriminatory of word embeddings.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41365337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-23DOI: 10.1017/s1351324922000420
Adam Stiff, Michael White, E. Fosler-Lussier, Lifeng Jin, Evan Jaffe, D. Danforth
Randomized prospective studies represent the gold standard for experimental design. In this paper, we present a randomized prospective study to validate the benefits of combining rule-based and data-driven natural language understanding methods in a virtual patient dialogue system. The system uses a rule-based pattern matching approach together with a machine learning (ML) approach in the form of a text-based convolutional neural network, combining the two methods with a simple logistic regression model to choose between their predictions for each dialogue turn. In an earlier, retrospective study, the hybrid system yielded a nearly 50% error reduction on our initial data, in part due to the differential performance between the two methods as a function of label frequency. Given these gains, and considering that our hybrid approach is unique among virtual patient systems, we compare the hybrid system to the rule-based system by itself in a randomized prospective study. We evaluate 110 unique medical student subjects interacting with the system over 5,296 conversation turns, to verify whether similar gains are observed in a deployed system. This prospective study broadly confirms the findings from the earlier one but also highlights important deficits in our training data. The hybrid approach still improves over either rule-based or ML approaches individually, even handling unseen classes with some success. However, we observe that live subjects ask more out-of-scope questions than expected. To better handle such questions, we investigate several modifications to the system combination component. These show significant overall accuracy improvements and modest F1 improvements on out-of-scope queries in an offline evaluation. We provide further analysis to characterize the difficulty of the out-of-scope problem that we have identified, as well as to suggest future improvements over the baseline we establish here.
{"title":"A randomized prospective study of a hybrid rule- and data-driven virtual patient","authors":"Adam Stiff, Michael White, E. Fosler-Lussier, Lifeng Jin, Evan Jaffe, D. Danforth","doi":"10.1017/s1351324922000420","DOIUrl":"https://doi.org/10.1017/s1351324922000420","url":null,"abstract":"\u0000 Randomized prospective studies represent the gold standard for experimental design. In this paper, we present a randomized prospective study to validate the benefits of combining rule-based and data-driven natural language understanding methods in a virtual patient dialogue system. The system uses a rule-based pattern matching approach together with a machine learning (ML) approach in the form of a text-based convolutional neural network, combining the two methods with a simple logistic regression model to choose between their predictions for each dialogue turn. In an earlier, retrospective study, the hybrid system yielded a nearly 50% error reduction on our initial data, in part due to the differential performance between the two methods as a function of label frequency. Given these gains, and considering that our hybrid approach is unique among virtual patient systems, we compare the hybrid system to the rule-based system by itself in a randomized prospective study. We evaluate 110 unique medical student subjects interacting with the system over 5,296 conversation turns, to verify whether similar gains are observed in a deployed system. This prospective study broadly confirms the findings from the earlier one but also highlights important deficits in our training data. The hybrid approach still improves over either rule-based or ML approaches individually, even handling unseen classes with some success. However, we observe that live subjects ask more out-of-scope questions than expected. To better handle such questions, we investigate several modifications to the system combination component. These show significant overall accuracy improvements and modest F1 improvements on out-of-scope queries in an offline evaluation. We provide further analysis to characterize the difficulty of the out-of-scope problem that we have identified, as well as to suggest future improvements over the baseline we establish here.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47119709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-12DOI: 10.1017/s1351324922000432
Lingxian Bao, Patrik Lambert, Toni Badia
Deep neural networks as an end-to-end approach lack robustness from an application point of view, as it is very difficult to fix an obvious problem without retraining the model, for example, when a model consistently predicts positive when seeing the word “terrible.” Meanwhile, it is less stressed that the commonly used attention mechanism is likely to “over-fit” by being overly sparse, so that some key positions in the input sequence could be overlooked by the network. To address these problems, we proposed a lexicon-enhanced attention LSTM model in 2019, named ATLX. In this paper, we describe extended experiments and analysis of the ATLX model. And, we also try to further improve the aspect-based sentiment analysis system by combining a vector-based sentiment domain adaptation method.
{"title":"Improving aspect-based neural sentiment classification with lexicon enhancement, attention regularization and sentiment induction","authors":"Lingxian Bao, Patrik Lambert, Toni Badia","doi":"10.1017/s1351324922000432","DOIUrl":"https://doi.org/10.1017/s1351324922000432","url":null,"abstract":"\u0000 Deep neural networks as an end-to-end approach lack robustness from an application point of view, as it is very difficult to fix an obvious problem without retraining the model, for example, when a model consistently predicts positive when seeing the word “terrible.” Meanwhile, it is less stressed that the commonly used attention mechanism is likely to “over-fit” by being overly sparse, so that some key positions in the input sequence could be overlooked by the network. To address these problems, we proposed a lexicon-enhanced attention LSTM model in 2019, named ATLX. In this paper, we describe extended experiments and analysis of the ATLX model. And, we also try to further improve the aspect-based sentiment analysis system by combining a vector-based sentiment domain adaptation method.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46177621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-07DOI: 10.1017/s1351324922000341
Kanishk Verma, Maja Popovic, Alexandros Poulis, Y. Cherkasova, Cathal Ó hÓbáin, A. Mazzone, Tijana Milosevic, Brian Davis
Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.
{"title":"Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents","authors":"Kanishk Verma, Maja Popovic, Alexandros Poulis, Y. Cherkasova, Cathal Ó hÓbáin, A. Mazzone, Tijana Milosevic, Brian Davis","doi":"10.1017/s1351324922000341","DOIUrl":"https://doi.org/10.1017/s1351324922000341","url":null,"abstract":"\u0000 Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41972550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1017/S1351324922000419
Beáta Lőrincz, E. Irimia, Adriana Stan, Verginica Barbu Mititelu
Abstract In this article, we introduce an extended, freely available resource for the Romanian language, named RoLEX. The dataset was developed mainly for speech processing applications, yet its applicability extends beyond this domain. RoLEX includes over 330,000 curated entries with information regarding lemma, morphosyntactic description, syllabification, lexical stress and phonemic transcription. The process of selecting the list of word entries and semi-automatically annotating the complete lexical information associated with each of the entries is thoroughly described. The dataset’s inherent knowledge is then evaluated in a task of concurrent prediction of syllabification, lexical stress marking and phonemic transcription. The evaluation looked into several dataset design factors, such as the minimum viable number of entries for correct prediction, the optimisation of the minimum number of required entries through expert selection and the augmentation of the input with morphosyntactic information, as well as the influence of each task in the overall accuracy. The best results were obtained when the orthographic form of the entries was augmented with the complete morphosyntactic tags. A word error rate of 3.08% and a character error rate of 1.08% were obtained this way. We show that using a carefully selected subset of entries for training can result in a similar performance to the performance obtained by a larger set of randomly selected entries (twice as many). In terms of prediction complexity, the lexical stress marking posed most problems and accounts for around 60% of the errors in the predicted sequence.
{"title":"RoLEX: The development of an extended Romanian lexical dataset and its evaluation at predicting concurrent lexical information","authors":"Beáta Lőrincz, E. Irimia, Adriana Stan, Verginica Barbu Mititelu","doi":"10.1017/S1351324922000419","DOIUrl":"https://doi.org/10.1017/S1351324922000419","url":null,"abstract":"Abstract In this article, we introduce an extended, freely available resource for the Romanian language, named RoLEX. The dataset was developed mainly for speech processing applications, yet its applicability extends beyond this domain. RoLEX includes over 330,000 curated entries with information regarding lemma, morphosyntactic description, syllabification, lexical stress and phonemic transcription. The process of selecting the list of word entries and semi-automatically annotating the complete lexical information associated with each of the entries is thoroughly described. The dataset’s inherent knowledge is then evaluated in a task of concurrent prediction of syllabification, lexical stress marking and phonemic transcription. The evaluation looked into several dataset design factors, such as the minimum viable number of entries for correct prediction, the optimisation of the minimum number of required entries through expert selection and the augmentation of the input with morphosyntactic information, as well as the influence of each task in the overall accuracy. The best results were obtained when the orthographic form of the entries was augmented with the complete morphosyntactic tags. A word error rate of 3.08% and a character error rate of 1.08% were obtained this way. We show that using a carefully selected subset of entries for training can result in a similar performance to the performance obtained by a larger set of randomly selected entries (twice as many). In terms of prediction complexity, the lexical stress marking posed most problems and accounts for around 60% of the errors in the predicted sequence.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48763416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1017/s1351324922000390
Mojdeh Hashemi-Namin, M. Jahed-Motlagh, Adel Torkaman Rahmani
Abstract Text-to-scene conversion systems map natural language text to formal representations required for visual scenes. The difficulty involved in this mapping is one of the most critical challenges for developing these systems. The current study mapped Persian natural language text as the headmost system to a conceptual scene model. This conceptual scene model is an intermediate semantic representation between natural language and the visual scene and contains descriptions of visual elements of the scene. It will be used to produce meaningful animation based on an input story in this ongoing study. The mapping task was modeled as a sequential labeling problem, and a conditional random field (CRF) model was trained and tested for sequential labeling of scene model elements. To the best of the authors’ knowledge, no dataset for this task exists; thus, the required dataset was collected for this task. The lack of required off-the-shelf natural language processing modules and a significant error rate in the available corpora were important challenges to dataset collection. Some features of the dataset were manually annotated. The results were evaluated using standard text classification metrics, and an average accuracy of 85.7% was obtained, which is satisfactory.
{"title":"Recognition of visual scene elements from a story text in Persian natural language","authors":"Mojdeh Hashemi-Namin, M. Jahed-Motlagh, Adel Torkaman Rahmani","doi":"10.1017/s1351324922000390","DOIUrl":"https://doi.org/10.1017/s1351324922000390","url":null,"abstract":"Abstract Text-to-scene conversion systems map natural language text to formal representations required for visual scenes. The difficulty involved in this mapping is one of the most critical challenges for developing these systems. The current study mapped Persian natural language text as the headmost system to a conceptual scene model. This conceptual scene model is an intermediate semantic representation between natural language and the visual scene and contains descriptions of visual elements of the scene. It will be used to produce meaningful animation based on an input story in this ongoing study. The mapping task was modeled as a sequential labeling problem, and a conditional random field (CRF) model was trained and tested for sequential labeling of scene model elements. To the best of the authors’ knowledge, no dataset for this task exists; thus, the required dataset was collected for this task. The lack of required off-the-shelf natural language processing modules and a significant error rate in the available corpora were important challenges to dataset collection. Some features of the dataset were manually annotated. The results were evaluated using standard text classification metrics, and an average accuracy of 85.7% was obtained, which is satisfactory.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47512224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-22DOI: 10.1017/s1351324922000353
Nikola Ljubesic, I. Mozetič, Petra Kralj Novak
The quality of annotations in manually annotated hate speech datasets is crucial for automatic hate speech detection. This contribution focuses on the positive effects of manually annotating online comments for hate speech within the context in which the comments occur. We quantify the impact of context availability by meticulously designing an experiment: Two annotation rounds are performed, one in-context and one out-of-context, on the same English YouTube data (more than 10,000 comments), by using the same annotation schema and platform, the same highly trained annotators, and quantifying annotation quality through inter-annotator agreement. Our results show that the presence of context has a significant positive impact on the quality of the manual annotations. This positive impact is more noticeable among replies than among comments, although the former is harder to consistently annotate overall. Previous research reporting that out-of-context annotations favour assigning non-hate-speech labels is also corroborated, showing further that this tendency is especially present among comments inciting violence, a highly relevant category for hate speech research and society overall. We believe that this work will improve future annotation campaigns even beyond hate speech and motivate further research on the highly relevant questions of data annotation methodology in natural language processing, especially in the light of the current expansion of its scope of application.
{"title":"Quantifying the impact of context on the quality of manual hate speech annotation","authors":"Nikola Ljubesic, I. Mozetič, Petra Kralj Novak","doi":"10.1017/s1351324922000353","DOIUrl":"https://doi.org/10.1017/s1351324922000353","url":null,"abstract":"\u0000 The quality of annotations in manually annotated hate speech datasets is crucial for automatic hate speech detection. This contribution focuses on the positive effects of manually annotating online comments for hate speech within the context in which the comments occur. We quantify the impact of context availability by meticulously designing an experiment: Two annotation rounds are performed, one in-context and one out-of-context, on the same English YouTube data (more than 10,000 comments), by using the same annotation schema and platform, the same highly trained annotators, and quantifying annotation quality through inter-annotator agreement. Our results show that the presence of context has a significant positive impact on the quality of the manual annotations. This positive impact is more noticeable among replies than among comments, although the former is harder to consistently annotate overall. Previous research reporting that out-of-context annotations favour assigning non-hate-speech labels is also corroborated, showing further that this tendency is especially present among comments inciting violence, a highly relevant category for hate speech research and society overall. We believe that this work will improve future annotation campaigns even beyond hate speech and motivate further research on the highly relevant questions of data annotation methodology in natural language processing, especially in the light of the current expansion of its scope of application.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47630033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-09DOI: 10.1017/s1351324922000389
{"title":"NLE volume 28 issue 5 Cover and Back matter","authors":"","doi":"10.1017/s1351324922000389","DOIUrl":"https://doi.org/10.1017/s1351324922000389","url":null,"abstract":"","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45308889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-09DOI: 10.1017/S1351324922000365
Kenneth Ward Church
Abstract Deep nets are becoming larger and larger in practice, with no respect for (non)-factors that ought to limit growth including the so-called curse of dimensionality (CoD). Donoho suggested that dimensionality can be a blessing as well as a curse. Current practice in industry is well ahead of theory, but there are some recent theoretical results from Weinan E’s group suggesting that errors may be independent of dimensions $d$. Current practice suggests an even stronger conjecture: deep nets are not merely immune to CoD, but actually, deep nets thrive on scale.
{"title":"Emerging trends: Deep nets thrive on scale","authors":"Kenneth Ward Church","doi":"10.1017/S1351324922000365","DOIUrl":"https://doi.org/10.1017/S1351324922000365","url":null,"abstract":"Abstract Deep nets are becoming larger and larger in practice, with no respect for (non)-factors that ought to limit growth including the so-called curse of dimensionality (CoD). Donoho suggested that dimensionality can be a blessing as well as a curse. Current practice in industry is well ahead of theory, but there are some recent theoretical results from Weinan E’s group suggesting that errors may be independent of dimensions $d$. Current practice suggests an even stronger conjecture: deep nets are not merely immune to CoD, but actually, deep nets thrive on scale.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46832056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-09DOI: 10.1017/s1351324922000377
R. Mitkov, B. Boguraev
whether or trans-lation, computer science or engineering. Its is to the computational linguistics research and the implementation of practical applications with potential real-world use. As well as publishing original research articles on a broad range of topics - from text analy- sis, machine translation, information retrieval, speech processing and generation to integrated systems and multi-modal interfaces - it also publishes special issues on specific natural language processing methods, tasks or applications. The journal welcomes survey papers describing the state of the art of a specific topic. The Journal of Natural Language Engineering also publishes the popular Industry Watch and Emerging Trends columns as well as book reviews.
{"title":"NLE volume 28 issue 5 Cover and Front matter","authors":"R. Mitkov, B. Boguraev","doi":"10.1017/s1351324922000377","DOIUrl":"https://doi.org/10.1017/s1351324922000377","url":null,"abstract":"whether or trans-lation, computer science or engineering. Its is to the computational linguistics research and the implementation of practical applications with potential real-world use. As well as publishing original research articles on a broad range of topics - from text analy- sis, machine translation, information retrieval, speech processing and generation to integrated systems and multi-modal interfaces - it also publishes special issues on specific natural language processing methods, tasks or applications. The journal welcomes survey papers describing the state of the art of a specific topic. The Journal of Natural Language Engineering also publishes the popular Industry Watch and Emerging Trends columns as well as book reviews.","PeriodicalId":49143,"journal":{"name":"Natural Language Engineering","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43571198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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