North American Chapter of the Association for Computational Linguistics最新文献_第4页

Fast and Light-Weight Answer Text Retrieval in Dialogue Systems 对话系统中快速轻量级的答案文本检索

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-27 DOI: 10.48550/arXiv.2205.14226

H. Wan, S. Patel, J. William Murdock, Saloni Potdar, Sachindra Joshi

引用次数: 0

Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation 基于对话的常识和命名实体感知知识生成

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-27 DOI: 10.48550/arXiv.2205.13928

Deeksha Varshney, Akshara Prabhakar, Asif Ekbal

{"title":"Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation","authors":"Deeksha Varshney, Akshara Prabhakar, Asif Ekbal","doi":"10.48550/arXiv.2205.13928","DOIUrl":"https://doi.org/10.48550/arXiv.2205.13928","url":null,"abstract":"Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-reference is critical for dialogue comprehension and generation. In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance. We enhance the commonsense knowledge with named entity-aware structures using co-references. Our proposed model utilizes a multi-hop attention layer to preserve the most accurate and critical parts of the dialogue history and the associated knowledge. In addition, we employ a Commonsense and Named Entity Enhanced Attention Module, which starts with the extracted triples from various sources and gradually finds the relevant supporting set of triples using multi-hop attention with the query vector obtained from the interactive dialogue-knowledge module. Empirical results on two benchmark datasets demonstrate that our model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment. Our code is publicly available at https://github.com/deekshaVarshney/CNTF; https://www.iitp.ac.in/-ai-nlp-ml/resources/codes/CNTF.zip.","PeriodicalId":382084,"journal":{"name":"North American Chapter of the Association for Computational Linguistics","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032214","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: 9

Learning Dialogue Representations from Consecutive Utterances 从连续话语中学习对话表征

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-26 DOI: 10.48550/arXiv.2205.13568

Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew O. Arnold, Bing Xiang

{"title":"Learning Dialogue Representations from Consecutive Utterances","authors":"Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall, Xiaofei Ma, Andrew O. Arnold, Bing Xiang","doi":"10.48550/arXiv.2205.13568","DOIUrl":"https://doi.org/10.48550/arXiv.2205.13568","url":null,"abstract":"Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks. DSE learns from dialogues by taking consecutive utterances of the same dialogue as positive pairs for contrastive learning. Despite its simplicity, DSE achieves significantly better representation capability than other dialogue representation and universal sentence representation models. We evaluate DSE on five downstream dialogue tasks that examine dialogue representation at different semantic granularities. Experiments in few-shot and zero-shot settings show that DSE outperforms baselines by a large margin, for example, it achieves 13% average performance improvement over the strongest unsupervised baseline in 1-shot intent classification on 6 datasets. We also provide analyses on the benefits and limitations of our model.","PeriodicalId":382084,"journal":{"name":"North American Chapter of the Association for Computational Linguistics","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122899542","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: 7

Low Resource Style Transfer via Domain Adaptive Meta Learning 基于领域自适应元学习的低资源风格迁移

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-25 DOI: 10.48550/arXiv.2205.12475

Xiangyang Li, Xiang Long, Yu Xia, Sujian Li

引用次数: 5

Partial-input baselines show that NLI models can ignore context, but they don’t. 部分输入基线显示NLI模型可以忽略上下文，但事实并非如此。

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-24 DOI: 10.48550/arXiv.2205.12181

Neha Srikanth, Rachel Rudinger

引用次数: 2

Interpretable Proof Generation via Iterative Backward Reasoning 通过迭代逆向推理生成可解释的证明

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-22 DOI: 10.48550/arXiv.2205.10714

Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, Ruifeng Xu

{"title":"Interpretable Proof Generation via Iterative Backward Reasoning","authors":"Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, Ruifeng Xu","doi":"10.48550/arXiv.2205.10714","DOIUrl":"https://doi.org/10.48550/arXiv.2205.10714","url":null,"abstract":"We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer. We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and history paths, without any intermediate texts that may introduce external noise during proof generation. There are three main modules in IBR, QA and proof strategy prediction to obtain the answer and offer guidance for the following procedure; parent node prediction to determine a node in the existing proof that a new child node will link to; child node prediction to find out which new node will be added to the proof. Experiments on both synthetic and paraphrased datasets demonstrate that IBR has better in-domain performance as well as cross-domain transferability than several strong baselines. Our code and models are available at https://github. com/find-knowledge/IBR.","PeriodicalId":382084,"journal":{"name":"North American Chapter of the Association for Computational Linguistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128868803","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: 11

Modeling Exemplification in Long-form Question Answering via Retrieval 基于检索的长格式问答建模例证

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-19 DOI: 10.48550/arXiv.2205.09278

Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, Mohit Iyyer

{"title":"Modeling Exemplification in Long-form Question Answering via Retrieval","authors":"Shufan Wang, Fangyuan Xu, Laure Thompson, Eunsol Choi, Mohit Iyyer","doi":"10.48550/arXiv.2205.09278","DOIUrl":"https://doi.org/10.48550/arXiv.2205.09278","url":null,"abstract":"Exemplification is a process by which writers explain or clarify a concept by providing an example. While common in all forms of writing, exemplification is particularly useful in the task of long-form question answering (LFQA), where a complicated answer can be made more understandable through simple examples. In this paper, we provide the first computational study of exemplification in QA, performing a fine-grained annotation of different types of examples (e.g., hypotheticals, anecdotes) in three corpora. We show that not only do state-of-the-art LFQA models struggle to generate relevant examples, but also that standard evaluation metrics such as ROUGE are insufficient to judge exemplification quality. We propose to treat exemplification as a retrieval problem in which a partially-written answer is used to query a large set of human-written examples extracted from a corpus. Our approach allows a reliable ranking-type automatic metrics that correlates well with human evaluation. A human evaluation shows that our model’s retrieved examples are more relevant than examples generated from a state-of-the-art LFQA model.","PeriodicalId":382084,"journal":{"name":"North American Chapter of the Association for Computational Linguistics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376598","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: 6

CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning CREATER: ctrl驱动的广告文本生成与控制的预训练和对比微调

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-18 DOI: 10.48550/arXiv.2205.08943

Penghui Wei, Xuanhua Yang, Shaoguo Liu, Liang Wang, Bo Zheng

引用次数: 7

SKILL: Structured Knowledge Infusion for Large Language Models 技能:大型语言模型的结构化知识注入

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-17 DOI: 10.18653/v1/2022.naacl-main.113

Fedor Moiseev, Zhe Dong, Enrique Alfonseca, Martin Jaggi

引用次数: 28

Generic and Trend-aware Curriculum Learning for Relation Extraction 面向关系抽取的通用和趋势感知课程学习

North American Chapter of the Association for Computational Linguistics Pub Date : 2022-05-17 DOI: 10.48550/arXiv.2205.08625

Nidhi Vakil, Hadi Amiri

引用次数: 6