arXiv - CS - Computation and Language最新文献_第7页

The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal CLC-UKET 数据集：英国就业法庭案件结果预测基准

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.08098

Huiyuan Xie, Felix Steffek, Joana Ribeiro de Faria, Christine Carter, Jonathan Rutherford

{"title":"The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal","authors":"Huiyuan Xie, Felix Steffek, Joana Ribeiro de Faria, Christine Carter, Jonathan Rutherford","doi":"arxiv-2409.08098","DOIUrl":"https://doi.org/arxiv-2409.08098","url":null,"abstract":"This paper explores the intersection of technological innovation and access\u0000to justice by developing a benchmark for predicting case outcomes in the UK\u0000Employment Tribunal (UKET). To address the challenge of extensive manual\u0000annotation, the study employs a large language model (LLM) for automatic\u0000annotation, resulting in the creation of the CLC-UKET dataset. The dataset\u0000consists of approximately 19,000 UKET cases and their metadata. Comprehensive\u0000legal annotations cover facts, claims, precedent references, statutory\u0000references, case outcomes, reasons and jurisdiction codes. Facilitated by the\u0000CLC-UKET data, we examine a multi-class case outcome prediction task in the\u0000UKET. Human predictions are collected to establish a performance reference for\u0000model comparison. Empirical results from baseline models indicate that\u0000finetuned transformer models outperform zero-shot and few-shot LLMs on the UKET\u0000prediction task. The performance of zero-shot LLMs can be enhanced by\u0000integrating task-related information into few-shot examples. We hope that the\u0000CLC-UKET dataset, along with human annotations and empirical findings, can\u0000serve as a valuable benchmark for employment-related dispute resolution.","PeriodicalId":501030,"journal":{"name":"arXiv - CS - Computation and Language","volume":"157 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184413","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

TravelAgent: An AI Assistant for Personalized Travel Planning TravelAgent：个性化旅行规划的人工智能助手

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.08069

Aili Chen, Xuyang Ge, Ziquan Fu, Yanghua Xiao, Jiangjie Chen

引用次数: 0

Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources Source2Synth：基于真实数据源的合成数据生成和整理

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.08239

Alisia Lupidi, Carlos Gemmell, Nicola Cancedda, Jane Dwivedi-Yu, Jason Weston, Jakob Foerster, Roberta Raileanu, Maria Lomeli

引用次数: 0

Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation 支持在线讨论：将人工智能融入adhocracy+参与平台，以提高议事能力

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.07780

Maike Behrendt, Stefan Sylvius Wagner, Stefan Harmeling

引用次数: 0

An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting 基于语句重写的无监督对话主题分割模型

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.07672

Xia Hou, Qifeng Li, Tongliang Li

{"title":"An Unsupervised Dialogue Topic Segmentation Model Based on Utterance Rewriting","authors":"Xia Hou, Qifeng Li, Tongliang Li","doi":"arxiv-2409.07672","DOIUrl":"https://doi.org/arxiv-2409.07672","url":null,"abstract":"Dialogue topic segmentation plays a crucial role in various types of dialogue\u0000modeling tasks. The state-of-the-art unsupervised DTS methods learn topic-aware\u0000discourse representations from conversation data through adjacent discourse\u0000matching and pseudo segmentation to further mine useful clues in unlabeled\u0000conversational relations. However, in multi-round dialogs, discourses often\u0000have co-references or omissions, leading to the fact that direct use of these\u0000discourses for representation learning may negatively affect the semantic\u0000similarity computation in the neighboring discourse matching task. In order to\u0000fully utilize the useful cues in conversational relations, this study proposes\u0000a novel unsupervised dialog topic segmentation method that combines the\u0000Utterance Rewriting (UR) technique with an unsupervised learning algorithm to\u0000efficiently utilize the useful cues in unlabeled dialogs by rewriting the\u0000dialogs in order to recover the co-referents and omitted words. Compared with\u0000existing unsupervised models, the proposed Discourse Rewriting Topic\u0000Segmentation Model (UR-DTS) significantly improves the accuracy of topic\u0000segmentation. The main finding is that the performance on DialSeg711 improves\u0000by about 6% in terms of absolute error score and WD, achieving 11.42% in terms\u0000of absolute error score and 12.97% in terms of WD. on Doc2Dial the absolute\u0000error score and WD improves by about 3% and 2%, respectively, resulting in SOTA\u0000reaching 35.17% in terms of absolute error score and 38.49% in terms of WD.\u0000This shows that the model is very effective in capturing the nuances of\u0000conversational topics, as well as the usefulness and challenges of utilizing\u0000unlabeled conversations.","PeriodicalId":501030,"journal":{"name":"arXiv - CS - Computation and Language","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184439","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

WhisperNER: Unified Open Named Entity and Speech Recognition WhisperNER：统一开放式命名实体和语音识别

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.08107

Gil Ayache, Menachem Pirchi, Aviv Navon, Aviv Shamsian, Gill Hetz, Joseph Keshet

引用次数: 0

Multi-object event graph representation learning for Video Question Answering 用于视频问题解答的多对象事件图表示学习

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.07747

Yanan Wang, Shuichiro Haruta, Donghuo Zeng, Julio Vizcarra, Mori Kurokawa

{"title":"Multi-object event graph representation learning for Video Question Answering","authors":"Yanan Wang, Shuichiro Haruta, Donghuo Zeng, Julio Vizcarra, Mori Kurokawa","doi":"arxiv-2409.07747","DOIUrl":"https://doi.org/arxiv-2409.07747","url":null,"abstract":"Video question answering (VideoQA) is a task to predict the correct answer to\u0000questions posed about a given video. The system must comprehend spatial and\u0000temporal relationships among objects extracted from videos to perform causal\u0000and temporal reasoning. While prior works have focused on modeling individual\u0000object movements using transformer-based methods, they falter when capturing\u0000complex scenarios involving multiple objects (e.g., \"a boy is throwing a ball\u0000in a hoop\"). We propose a contrastive language event graph representation\u0000learning method called CLanG to address this limitation. Aiming to capture\u0000event representations associated with multiple objects, our method employs a\u0000multi-layer GNN-cluster module for adversarial graph representation learning,\u0000enabling contrastive learning between the question text and its relevant\u0000multi-object event graph. Our method outperforms a strong baseline, achieving\u0000up to 2.2% higher accuracy on two challenging VideoQA datasets, NExT-QA and\u0000TGIF-QA-R. In particular, it is 2.8% better than baselines in handling causal\u0000and temporal questions, highlighting its strength in reasoning multiple\u0000object-based events.","PeriodicalId":501030,"journal":{"name":"arXiv - CS - Computation and Language","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184443","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

Stable Language Model Pre-training by Reducing Embedding Variability 通过减少嵌入变异性实现稳定的语言模型预训练

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.07787

Woojin Chung, Jiwoo Hong, Na Min An, James Thorne, Se-Young Yun

引用次数: 0

On the Role of Context in Reading Time Prediction 论语境在阅读时间预测中的作用

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.08160

Andreas Opedal, Eleanor Chodroff, Ryan Cotterell, Ethan Gotlieb Wilcox

{"title":"On the Role of Context in Reading Time Prediction","authors":"Andreas Opedal, Eleanor Chodroff, Ryan Cotterell, Ethan Gotlieb Wilcox","doi":"arxiv-2409.08160","DOIUrl":"https://doi.org/arxiv-2409.08160","url":null,"abstract":"We present a new perspective on how readers integrate context during\u0000real-time language comprehension. Our proposals build on surprisal theory,\u0000which posits that the processing effort of a linguistic unit (e.g., a word) is\u0000an affine function of its in-context information content. We first observe that\u0000surprisal is only one out of many potential ways that a contextual predictor\u0000can be derived from a language model. Another one is the pointwise mutual\u0000information (PMI) between a unit and its context, which turns out to yield the\u0000same predictive power as surprisal when controlling for unigram frequency.\u0000Moreover, both PMI and surprisal are correlated with frequency. This means that\u0000neither PMI nor surprisal contains information about context alone. In response\u0000to this, we propose a technique where we project surprisal onto the orthogonal\u0000complement of frequency, yielding a new contextual predictor that is\u0000uncorrelated with frequency. Our experiments show that the proportion of\u0000variance in reading times explained by context is a lot smaller when context is\u0000represented by the orthogonalized predictor. From an interpretability\u0000standpoint, this indicates that previous studies may have overstated the role\u0000that context has in predicting reading times.","PeriodicalId":501030,"journal":{"name":"arXiv - CS - Computation and Language","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184441","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

Learning Rules from KGs Guided by Language Models 在语言模型指导下从幼稚园学习规则

arXiv - CS - Computation and Language Pub Date : 2024-09-12 DOI: arxiv-2409.07869

Zihang Peng, Daria Stepanova, Vinh Thinh Ho, Heike Adel, Alessandra Russo, Simon Ott

{"title":"Learning Rules from KGs Guided by Language Models","authors":"Zihang Peng, Daria Stepanova, Vinh Thinh Ho, Heike Adel, Alessandra Russo, Simon Ott","doi":"arxiv-2409.07869","DOIUrl":"https://doi.org/arxiv-2409.07869","url":null,"abstract":"Advances in information extraction have enabled the automatic construction of\u0000large knowledge graphs (e.g., Yago, Wikidata or Google KG), which are widely\u0000used in many applications like semantic search or data analytics. However, due\u0000to their semi-automatic construction, KGs are often incomplete. Rule learning\u0000methods, concerned with the extraction of frequent patterns from KGs and\u0000casting them into rules, can be applied to predict potentially missing facts. A\u0000crucial step in this process is rule ranking. Ranking of rules is especially\u0000challenging over highly incomplete or biased KGs (e.g., KGs predominantly\u0000storing facts about famous people), as in this case biased rules might fit the\u0000data best and be ranked at the top based on standard statistical metrics like\u0000rule confidence. To address this issue, prior works proposed to rank rules not\u0000only relying on the original KG but also facts predicted by a KG embedding\u0000model. At the same time, with the recent rise of Language Models (LMs), several\u0000works have claimed that LMs can be used as alternative means for KG completion.\u0000In this work, our goal is to verify to which extent the exploitation of LMs is\u0000helpful for improving the quality of rule learning systems.","PeriodicalId":501030,"journal":{"name":"arXiv - CS - Computation and Language","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184433","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