Multi-Row, Multi-Span Distant Supervision For Table+Text Question

• URL: http://arxiv.org/abs/2112.07337v3
• Date: Sun, 11 Jun 2023 18:46:16 GMT
• Title: Multi-Row, Multi-Span Distant Supervision For Table+Text Question
• Authors: Vishwajeet Kumar, Yash Gupta, Saneem Chemmengath, Jaydeep Sen, Soumen Chakrabarti, Samarth Bharadwaj, FeiFei Pan
• Abstract summary: Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years. We present MITQA, a transformer-based TextTableQA system that is explicitly designed to cope with distant supervision along both these axes.
• Score: 33.809732338627136
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years, as tables are often found embedded in documents along with related text. HybridQA and OTT-QA are the two best-known TextTableQA datasets, with questions that are best answered by combining information from both table cells and linked text passages. A common challenge in both datasets, and TextTableQA in general, is that the training instances include just the question and answer, where the gold answer may match not only multiple table cells across table rows but also multiple text spans within the scope of a table row and its associated text. This leads to a noisy multi instance training regime. We present MITQA, a transformer-based TextTableQA system that is explicitly designed to cope with distant supervision along both these axes, through a multi-instance loss objective, together with careful curriculum design. Our experiments show that the proposed multi-instance distant supervision approach helps MITQA get state-of-the-art results beating the existing baselines for both HybridQA and OTT-QA, putting MITQA at the top of HybridQA leaderboard with best EM and F1 scores on a held out test set.

Related papers

• TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization [3.531533402602335]
  Multi-hop table-text QA requires multiple hops between the table and text. Our model uses an enhanced retriever for table-text information retrieval. Our experiments demonstrate the potential of prompt-based approaches.
  arXiv  Detail & Related papers  (2024-06-20T20:55:38Z)
• MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering [3.1651118728570635]
  In today's fast-paced industry, professionals face the challenge of summarizing a large number of documents and extracting vital information from them on a daily basis. To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information. Recent advancements in Large Language Models (LLMs) have opened up new possibilities for extracting information from tabular data using prompts.
  arXiv  Detail & Related papers  (2024-03-28T03:14:18Z)
• MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering [61.48881995121938]
  Real-world queries are complex in nature, often over multiple tables in a relational database or web page. Our model, MultiTabQA, not only answers questions over multiple tables, but also generalizes to generate tabular answers.
  arXiv  Detail & Related papers  (2023-05-22T08:25:15Z)
• PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance [96.06505049126345]
  We present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. UniPCQA performs multi-task learning over all sub-tasks in PCQA and incorporates a simple ensemble strategy to alleviate the error propagation issue in the multi-task learning by cross-validating top-$k$ sampled Seq2Seq
  arXiv  Detail & Related papers  (2022-10-17T08:06:56Z)
• Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA [85.17249272519626]
  An optimized OpenQA Table-Text Retriever (OTTeR) is proposed. We conduct retrieval-centric mixed-modality synthetic pre-training. OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.
  arXiv  Detail & Related papers  (2022-10-11T07:04:39Z)
• OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering [106.73213656603453]
  We develop a simple table-based QA model with minimal annotation effort. We propose an omnivorous pretraining approach that consumes both natural and synthetic data.
  arXiv  Detail & Related papers  (2022-07-08T01:23:45Z)
• CLTR: An End-to-End, Transformer-Based System for Cell Level Table Retrieval and Table Question Answering [8.389189333083513]
  We present the first end-to-end, transformer-based table question answering (QA) system. It takes natural language questions and massive table corpus as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question. We introduce two new open-domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural language questions over 76,242 tables.
  arXiv  Detail & Related papers  (2021-06-08T15:22:10Z)
• TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance [71.76018597965378]
  We build a new large-scale Question Answering dataset containing both Tabular And Textual data, named TAT-QA. We propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text.
  arXiv  Detail & Related papers  (2021-05-17T06:12:06Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)

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.