Reasoning over Logically Interacted Conditions for Question Answering
- URL: http://arxiv.org/abs/2205.12898v1
- Date: Wed, 25 May 2022 16:41:39 GMT
- Title: Reasoning over Logically Interacted Conditions for Question Answering
- Authors: Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
- Abstract summary: We study a more challenging task where answers are constrained by a list of conditions that logically interact.
We propose a new model, TReasoner, for this challenging reasoning task.
TReasoner achieves state-of-the-art performance on two benchmark conditional QA datasets.
- Score: 113.9231035680578
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Some questions have multiple answers that are not equally correct, i.e.
answers are different under different conditions. Conditions are used to
distinguish answers as well as to provide additional information to support
them. In this paper, we study a more challenging task where answers are
constrained by a list of conditions that logically interact, which requires
performing logical reasoning over the conditions to determine the correctness
of the answers. Even more challenging, we only provide evidences for a subset
of the conditions, so some questions may not have deterministic answers. In
such cases, models are asked to find probable answers and identify conditions
that need to be satisfied to make the answers correct. We propose a new model,
TReasoner, for this challenging reasoning task. TReasoner consists of an
entailment module, a reasoning module, and a generation module (if the answers
are free-form text spans). TReasoner achieves state-of-the-art performance on
two benchmark conditional QA datasets, outperforming the previous
state-of-the-art by 3-10 points.
Related papers
- Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering [34.599299893060895]
Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions.
Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing.
We propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document.
arXiv Detail & Related papers (2024-08-10T05:09:11Z) - MDCR: A Dataset for Multi-Document Conditional Reasoning [20.42067697305166]
ConditionalQA was proposed to evaluate models' capability of reading a document and answering eligibility questions, considering unmentioned conditions.
We propose a new dataset MDCR, which can reflect real-world challenges and serve as a new test bed for complex conditional reasoning that requires optimization.
arXiv Detail & Related papers (2024-06-17T17:38:43Z) - Controllable Decontextualization of Yes/No Question and Answers into
Factual Statements [28.02936811004903]
We address the problem of controllable rewriting of answers to polar questions into decontextualized and succinct factual statements.
We propose a Transformer sequence to sequence model that utilizes soft-constraints to ensure controllable rewriting.
arXiv Detail & Related papers (2024-01-18T07:52:12Z) - CREPE: Open-Domain Question Answering with False Presuppositions [92.20501870319765]
We introduce CREPE, a QA dataset containing a natural distribution of presupposition failures from online information-seeking forums.
We find that 25% of questions contain false presuppositions, and provide annotations for these presuppositions and their corrections.
We show that adaptations of existing open-domain QA models can find presuppositions moderately well, but struggle when predicting whether a presupposition is factually correct.
arXiv Detail & Related papers (2022-11-30T18:54:49Z) - WikiWhy: Answering and Explaining Cause-and-Effect Questions [62.60993594814305]
We introduce WikiWhy, a QA dataset built around explaining why an answer is true in natural language.
WikiWhy contains over 9,000 "why" question-answer-rationale triples, grounded on Wikipedia facts across a diverse set of topics.
GPT-3 baselines achieve only 38.7% human-evaluated correctness in the end-to-end answer & explain condition.
arXiv Detail & Related papers (2022-10-21T17:59:03Z) - Learn to Explain: Multimodal Reasoning via Thought Chains for Science
Question Answering [124.16250115608604]
We present Science Question Answering (SQA), a new benchmark that consists of 21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations.
We show that SQA improves the question answering performance by 1.20% in few-shot GPT-3 and 3.99% in fine-tuned UnifiedQA.
Our analysis further shows that language models, similar to humans, benefit from explanations to learn from fewer data and achieve the same performance with just 40% of the data.
arXiv Detail & Related papers (2022-09-20T07:04:24Z) - 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) - SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions [66.86887670416193]
We show that state-of-the-art VQA models have comparable performance in answering perception and reasoning questions, but suffer from consistency problems.
To address this shortcoming, we propose an approach called Sub-Question-aware Network Tuning (SQuINT)
We show that SQuINT improves model consistency by 5%, also marginally improving performance on the Reasoning questions in VQA, while also displaying better attention maps.
arXiv Detail & Related papers (2020-01-20T01:02:36Z)
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.