Exploring the State of the Art in Legal QA Systems

• URL: http://arxiv.org/abs/2304.06623v3
• Date: Fri, 15 Sep 2023 10:19:53 GMT
• Title: Exploring the State of the Art in Legal QA Systems
• Authors: Abdelrahman Abdallah, Bhawna Piryani, Adam Jatowt
• Abstract summary: Question answering (QA) systems are designed to generate answers to questions asked in human languages. QA has various practical applications, including customer service, education, research, and cross-lingual communication. We provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field.
• Score: 20.178251855026684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. Question answering (QA) systems are designed to generate answers to questions asked in human languages. QA uses natural language processing to understand questions and search through information to find relevant answers. QA has various practical applications, including customer service, education, research, and cross-lingual communication. However, QA faces challenges such as improving natural language understanding and handling complex and ambiguous questions. Answering questions related to the legal domain is a complex task, primarily due to the intricate nature and diverse range of legal document systems. Providing an accurate answer to a legal query typically necessitates specialized knowledge in the relevant domain, which makes this task all the more challenging, even for human experts. At this time, there is a lack of surveys that discuss legal question answering. To address this problem, we provide a comprehensive survey that reviews 14 benchmark datasets for question-answering in the legal field as well as presents a comprehensive review of the state-of-the-art Legal Question Answering deep learning models. We cover the different architectures and techniques used in these studies and the performance and limitations of these models. Moreover, we have established a public GitHub repository where we regularly upload the most recent articles, open data, and source code. The repository is available at: \url{https://github.com/abdoelsayed2016/Legal-Question-Answering-Review}.

Related papers

• Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models [10.834755282333589]
  Long-form Legal Question Answering dataset comprises 1,868 expert-annotated legal questions in the French language. Our experimental results demonstrate promising performance on automatic evaluation metrics. As one of the only comprehensive, expert-annotated long-form LQA dataset, LLeQA has the potential to not only accelerate research towards resolving a significant real-world issue, but also act as a rigorous benchmark for evaluating NLP models in specialized domains.
  arXiv  Detail & Related papers  (2023-09-29T08:23:19Z)
• ExpertQA: Expert-Curated Questions and Attributed Answers [51.68314045809179]
  We conduct human evaluation of responses from a few representative systems along various axes of attribution and factuality. We collect expert-curated questions from 484 participants across 32 fields of study, and then ask the same experts to evaluate generated responses to their own questions. The output of our analysis is ExpertQA, a high-quality long-form QA dataset with 2177 questions spanning 32 fields, along with verified answers and attributions for claims in the answers.
  arXiv  Detail & Related papers  (2023-09-14T16:54:34Z)
• Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions [95.92276099234344]
  We present a new state-of-the-art for answering ambiguous questions that exploits a database of unambiguous questions generated from Wikipedia. Our method improves performance by 15% on recall measures and 10% on measures which evaluate disambiguating questions from predicted outputs.
  arXiv  Detail & Related papers  (2023-08-16T20:23:16Z)
• Modern Question Answering Datasets and Benchmarks: A Survey [5.026863544662493]
  Question Answering (QA) is one of the most important natural language processing (NLP) tasks. It aims using NLP technologies to generate a corresponding answer to a given question based on the massive unstructured corpus. In this paper, we investigate influential QA datasets that have been released in the era of deep learning.
  arXiv  Detail & Related papers  (2022-06-30T05:53:56Z)
• Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices [0.0]
  The research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailed followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language.
  arXiv  Detail & Related papers  (2021-12-07T08:53:40Z)
• ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers [93.55268936974971]
  We describe a Question Answering dataset that contains complex questions with conditional answers. We call this dataset ConditionalQA. We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions.
  arXiv  Detail & Related papers  (2021-10-13T17:16:46Z)
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers [66.11048565324468]
  We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
  arXiv  Detail & Related papers  (2021-05-07T00:12:34Z)
• Technical Question Answering across Tasks and Domains [47.80330046038137]
  We present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-19T18:39:30Z)
• A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges [71.4531144086568]
  Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions. Researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference.
  arXiv  Detail & Related papers  (2020-07-26T07:13:32Z)

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.