Overview of the CAIL 2023 Argument Mining Track

• URL: http://arxiv.org/abs/2406.14503v1
• Date: Thu, 20 Jun 2024 17:06:13 GMT
• Title: Overview of the CAIL 2023 Argument Mining Track
• Abstract summary: The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. The track consists of two stages, and we introduce the tasks designed for each stage. We outline several submissions that achieve the best results, including their methods for different stages.
• Score: 46.70768209563857
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We give a detailed overview of the CAIL 2023 Argument Mining Track, one of the Chinese AI and Law Challenge (CAIL) 2023 tracks. The main goal of the track is to identify and extract interacting argument pairs in trial dialogs. It mainly uses summarized judgment documents but can also refer to trial recordings. The track consists of two stages, and we introduce the tasks designed for each stage; we also extend the data from previous events into a new dataset -- CAIL2023-ArgMine -- with annotated new cases from various causes of action. We outline several submissions that achieve the best results, including their methods for different stages. While all submissions rely on language models, they have incorporated strategies that may benefit future work in this field.

Related papers

• Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding [202.7892709083317]
  This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge.<n>The 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation.
  arXiv  Detail & Related papers  (2026-04-28T18:14:18Z)
• ICDAR 2025 Competition on End-to-End Document Image Machine Translation Towards Complex Layouts [47.61983500021015]
  Document Image Machine Translation (DIMT) seeks to translate text embedded in document images from one language to another.<n>The DIMT 2025 Challenge advances research on end-to-end document image translation.<n>The competition features two tracks, OCR-free and OCR-based, each with two subtasks for small (less than 1B parameters) and large (greater than 1B parameters) models.
  arXiv  Detail & Related papers  (2026-03-10T09:04:38Z)
• Overview of the TREC 2025 Tip-of-the-Tongue track [44.18900757791221]
  The TREC 2025 ToT track focused on a single ad-hoc retrieval task.<n>This year, we extended the track to general domain and incorporated different sets of test queries from diverse sources.
  arXiv  Detail & Related papers  (2026-01-28T14:51:52Z)
• Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks [62.443665295250035]
  We present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023) In total, 32 competing teams register for the challenge, from which we received 11 successful submissions.
  arXiv  Detail & Related papers  (2024-07-20T10:13:54Z)
• ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images [198.35937007558078]
  The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios.
  arXiv  Detail & Related papers  (2023-06-05T22:20:52Z)
• THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment [16.191450092389722]
  This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task. We try traditional lexical matching methods and pre-trained language models with different sizes. We get the third place in COLIEE 2023.
  arXiv  Detail & Related papers  (2023-05-11T14:11:48Z)
• IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks [59.457948080207174]
  In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks. Near 70k sentences in the dataset are fully annotated based on their argument properties. We propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE)
  arXiv  Detail & Related papers  (2022-03-23T08:07:32Z)
• ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization [41.68574396739112]
  We present ClidSum, a benchmark dataset for building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated summaries in different target languages.
  arXiv  Detail & Related papers  (2022-02-11T13:32:14Z)
• Multi-modal Visual Tracking: Review and Experimental Comparison [85.20414397784937]
  We summarize the multi-modal tracking algorithms, especially visible-depth (RGB-D) tracking and visible-thermal (RGB-T) tracking. We conduct experiments to analyze the effectiveness of trackers on five datasets.
  arXiv  Detail & Related papers  (2020-12-08T02:39:38Z)
• Knowledge-Aware Procedural Text Understanding with Multi-Stage Training [110.93934567725826]
  We focus on the task of procedural text understanding, which aims to comprehend such documents and track entities' states and locations during a process. Two challenges, the difficulty of commonsense reasoning and data insufficiency, still remain unsolved. We propose a novel KnOwledge-Aware proceduraL text understAnding (KOALA) model, which effectively leverages multiple forms of external knowledge.
  arXiv  Detail & Related papers  (2020-09-28T10:28:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.