Answer Consolidation: Formulation and Benchmarking

• URL: http://arxiv.org/abs/2205.00042v1
• Date: Fri, 29 Apr 2022 18:57:23 GMT
• Title: Answer Consolidation: Formulation and Benchmarking
• Authors: Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen
• Abstract summary: We formulate the problem of answer consolidation, where answers are partitioned into multiple groups. A comprehensive and non-redundant set of answers can be constructed by picking one answer from each group. Despite a promising performance achieved by the best-performing supervised models, we still believe this task has room for further improvements.
• Score: 35.38034364777484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation, where answers are partitioned into multiple groups, each representing different aspects of the answer set. Then, given this partitioning, a comprehensive and non-redundant set of answers can be constructed by picking one answer from each group. To initiate research on answer consolidation, we construct a dataset consisting of 4,699 questions and 24,006 sentences and evaluate multiple models. Despite a promising performance achieved by the best-performing supervised models, we still believe this task has room for further improvements.

Related papers

• Aspect-oriented Consumer Health Answer Summarization [2.298110639419913]
  Community Question-Answering (CQA) forums have revolutionized how people seek information, especially those related to their healthcare needs. There can be several answers in response to a single query, which makes it hard to grasp the key information related to the specific health concern. Our research focuses on aspect-based summarization of health answers to address this limitation.
  arXiv  Detail & Related papers  (2024-05-10T07:52:43Z)
• Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering [13.735277588793997]
  We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs) Our study focuses on a typical situations where users ask similar queries that involve identical mathematical reasoning steps and problem-solving procedures. We propose to improve the distributed synonymous questions using Self-Consistency (SC) and Chain-of-Thought (CoT) techniques.
  arXiv  Detail & Related papers  (2023-04-27T01:48:03Z)
• Successive Prompting for Decomposing Complex Questions [50.00659445976735]
  Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting. We introduce Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Our best model (with successive prompting) achieves an improvement of 5% absolute F1 on a few-shot version of the DROP dataset.
  arXiv  Detail & Related papers  (2022-12-08T06:03:38Z)
• Co-VQA : Answering by Interactive Sub Question Sequence [18.476819557695087]
  This paper proposes a conversation-based VQA framework, which consists of three components: Questioner, Oracle, and Answerer. To perform supervised learning for each model, we introduce a well-designed method to build a SQS for each question on VQA 2.0 and VQA-CP v2 datasets.
  arXiv  Detail & Related papers  (2022-04-02T15:09:16Z)
• AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization [73.91543616777064]
  Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. This work introduces a novel dataset of 4,631 CQA threads for answer summarization, curated by professional linguists.
  arXiv  Detail & Related papers  (2021-11-11T21:48:02Z)
• GooAQ: Open Question Answering with Diverse Answer Types [63.06454855313667]
  We present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google.
  arXiv  Detail & Related papers  (2021-04-18T05:40:39Z)
• Multi-Perspective Abstractive Answer Summarization [76.10437565615138]
  Community Question Answering forums contain a rich resource of answers to a wide range of questions. The goal of multi-perspective answer summarization is to produce a summary that includes all perspectives of the answer. This work introduces a novel dataset creation method to automatically create multi-perspective, bullet-point abstractive summaries.
  arXiv  Detail & Related papers  (2021-04-17T13:15:29Z)
• Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs [18.013010857062643]
  In community-based question answering platforms, it takes time for a user to get useful information from among many answers. This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers.
  arXiv  Detail & Related papers  (2020-11-18T07:33:03Z)
• Unsupervised Question Decomposition for Question Answering [102.56966847404287]
  We propose an algorithm for One-to-N Unsupervised Sequence Sequence (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions. We show large QA improvements on HotpotQA over a strong baseline on the original, out-of-domain, and multi-hop dev sets.
  arXiv  Detail & Related papers  (2020-02-22T19:40:35Z)

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.