Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)最新文献

That’s so cute!: The CARE Dataset for Affective Response Detection 太可爱了!:情感反应检测的CARE数据集

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 2022-01-28 DOI: 10.18653/v1/2022.conll-1.5

Jane A. Yu, A. Halevy

{"title":"That’s so cute!: The CARE Dataset for Affective Response Detection","authors":"Jane A. Yu, A. Halevy","doi":"10.18653/v1/2022.conll-1.5","DOIUrl":"https://doi.org/10.18653/v1/2022.conll-1.5","url":null,"abstract":"Social media plays an increasing role in our communication with friends and family, and in our consumption of entertainment and information. Hence, to design effective ranking functions for posts on social media, it would be useful to predict the affective responses of a post (e.g., whether it is likely to elicit feelings of entertainment, inspiration, or anger). Similar to work on emotion detection (which focuses on the affect of the publisher of the post), the traditional approach to recognizing affective response would involve an expensive investment in human annotation of training data. We create and publicly release CARE DB, a dataset of 230k social media post annotations according to seven affective responses using the Common Affective Response Expression (CARE) method. The CARE method is a means of leveraging the signal that is present in comments that are posted in response to a post, providing high-precision evidence about the affective response to the post without human annotation. Unlike human annotation, the annotation process we describe here can be iterated upon to expand the coverage of the method, particularly for new affective responses. We present experiments that demonstrate that the CARE annotations compare favorably with crowdsourced annotations. Finally, we use CARE DB to train competitive BERT-based models for predicting affective response as well as emotion detection, demonstrating the utility of the dataset for related tasks.","PeriodicalId":221345,"journal":{"name":"Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)","volume":"517 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123102955","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}

引用次数: 1

Enhancing the Transformer Decoder with Transition-based Syntax 用基于转换的语法增强Transformer解码器

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 2021-01-29 DOI: 10.18653/v1/2022.conll-1.27

Leshem Choshen, Omri Abend

引用次数: 1

Computational cognitive modeling of predictive sentence processing in a second language 第二语言预测句子处理的计算认知建模

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.23

Umesh Patil, Sol Lago

{"title":"Computational cognitive modeling of predictive sentence processing in a second language","authors":"Umesh Patil, Sol Lago","doi":"10.18653/v1/2022.conll-1.23","DOIUrl":"https://doi.org/10.18653/v1/2022.conll-1.23","url":null,"abstract":"We propose an ACT-R cue-based retrieval model of the real-time gender predictions displayed by second language (L2) learners. The model extends a previous model of native (L1) speakers according to two central accounts in L2 sentence processing: (i) the Interference Hypothesis, which proposes that retrieval interference is higher in L2 than L1 speakers; (ii) the Lexical Bottleneck Hypothesis, which proposes that problems with gender agreement are due to weak gender representations. We tested the predictions of these accounts using data from two visual world experiments, which found that the gender predictions elicited by German possessive pronouns were delayed and smaller in size in L2 than L1 speakers. The experiments also found a “match effect”, such that when the antecedent and possessee of the pronoun had the same gender, predictions were earlier than when the two genders differed. This match effect was smaller in L2 than L1 speakers. The model implementing the Lexical Bottleneck Hypothesis captured the effects of smaller predictions, smaller match effect and delayed predictions in one of the two conditions. By contrast, the model implementing the Interference Hypothesis captured the smaller prediction effect but it showed an earlier prediction effect and an increased match effect in L2 than L1 speakers. These results provide evidence for the Lexical Bottleneck Hypothesis, and they demonstrate a method for extending computational models of L1 to L2 processing.","PeriodicalId":221345,"journal":{"name":"Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622236","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}

引用次数: 0

On Language Spaces, Scales and Cross-Lingual Transfer of UD Parsers 论UD解析器的语言空间、尺度与跨语言迁移

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.18

T. Samardžić, Ximena Gutierrez-Vasques, Rob van der Goot, Max Müller-Eberstein, Olga Pelloni, Barbara Plank

引用次数: 1

Towards More Natural Artificial Languages 走向更自然的人工语言

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.7

Mark Hopkins

引用次数: 0

Combining Noisy Semantic Signals with Orthographic Cues: Cognate Induction for the Indic Dialect Continuum 结合嘈杂的语义信号与正字法线索:印度语方言连续体的同源归纳法

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.9

Niyati Bafna, Josef van Genabith, C. España-Bonet, Z. Žabokrtský

{"title":"Combining Noisy Semantic Signals with Orthographic Cues: Cognate Induction for the Indic Dialect Continuum","authors":"Niyati Bafna, Josef van Genabith, C. España-Bonet, Z. Žabokrtský","doi":"10.18653/v1/2022.conll-1.9","DOIUrl":"https://doi.org/10.18653/v1/2022.conll-1.9","url":null,"abstract":"We present a novel method for unsupervised cognate/borrowing identification from monolingual corpora designed for low and extremely low resource scenarios, based on combining noisy semantic signals from joint bilingual spaces with orthographic cues modelling sound change. We apply our method to the North Indian dialect continuum, containing several dozens of dialects and languages spoken by more than 100 million people. Many of these languages are zero-resource and therefore natural language processing for them is non-existent. We first collect monolingual data for 26 Indic languages, 16 of which were previously zero-resource, and perform exploratory character, lexical and subword cross-lingual alignment experiments for the first time at this scale on this dialect continuum. We create bilingual evaluation lexicons against Hindi for 20 of the languages. We then apply our cognate identification method on the data, and show that our method outperforms both traditional orthography baselines as well as EM-style learnt edit distance matrices. To the best of our knowledge, this is the first work to combine traditional orthographic cues with noisy bilingual embeddings to tackle unsupervised cognate detection in a (truly) low-resource setup, showing that even noisy bilingual embeddings can act as good guides for this task. We release our multilingual dialect corpus, called HinDialect, as well as our scripts for evaluation data collection and cognate induction.","PeriodicalId":221345,"journal":{"name":"Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128855373","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}

引用次数: 2

An Alignment-based Approach to Text Segmentation Similarity Scoring 一种基于对齐的文本分割相似度评分方法

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.26

Gerardo Ocampo Diaz, Jessica Ouyang

引用次数: 1

Shared knowledge in natural conversations: can entropy metrics shed light on information transfers? 自然对话中的共享知识:熵度量能揭示信息传递吗?

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.15

Eliot Maës, P. Blache, Leonor Becerra

引用次数: 0

Continual Learning for Natural Language Generations with Transformer Calibration 变压器校准自然语言世代的持续学习

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.4

Peng Yang, Dingcheng Li, Ping Li

{"title":"Continual Learning for Natural Language Generations with Transformer Calibration","authors":"Peng Yang, Dingcheng Li, Ping Li","doi":"10.18653/v1/2022.conll-1.4","DOIUrl":"https://doi.org/10.18653/v1/2022.conll-1.4","url":null,"abstract":"Conventional natural language process (NLP) generation models are trained offline with a given dataset for a particular task, which is referred to as isolated learning. Research on sequence-to-sequence language generation aims to study continual learning model to constantly learning from sequentially encountered tasks. However, continual learning studies often suffer from catastrophic forgetting, a persistent challenge for lifelong learning. In this paper, we present a novel NLP transformer model that attempts to mitigate catastrophic forgetting in online continual learning from a new perspective, i.e., attention calibration. We model the attention in the transformer as a calibrated unit in a general formulation, where the attention calibration could give benefits to balance the stability and plasticity of continual learning algorithms through influencing both their forward inference path and backward optimization path. Our empirical experiments, paraphrase generation and dialog response generation, demonstrate that this work outperforms state-of-the-art models by a considerable margin and effectively mitigate the forgetting.","PeriodicalId":221345,"journal":{"name":"Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125678736","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}

引用次数: 3

Incremental Processing of Principle B: Mismatches Between Neural Models and Humans 原理B的增量处理:神经模型与人之间的不匹配

Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.conll-1.11

Forrest Davis

引用次数: 0