Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs

• URL: http://arxiv.org/abs/2105.04850v1
• Date: Tue, 11 May 2021 08:08:35 GMT
• Title: Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs
• Authors: Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
• Abstract summary: We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations. Experiments show that CONQUER successfully learns to answer conversational questions from noisy reward signals.
• Score: 28.507683735633464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over knowledge graphs (KGs) can only learn from crisp question-answer pairs found in popular benchmarks. In reality, however, such training data is hard to come by: users would rarely mark answers explicitly as correct or wrong. In this work, we take a step towards a more natural learning paradigm - from noisy and implicit feedback via question reformulations. A reformulation is likely to be triggered by an incorrect system response, whereas a new follow-up question could be a positive signal on the previous turn's answer. We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations. CONQUER models the answering process as multiple agents walking in parallel on the KG, where the walks are determined by actions sampled using a policy network. This policy network takes the question along with the conversational context as inputs and is trained via noisy rewards obtained from the reformulation likelihood. To evaluate CONQUER, we create and release ConvRef, a benchmark with about 11k natural conversations containing around 205k reformulations. Experiments show that CONQUER successfully learns to answer conversational questions from noisy reward signals, significantly improving over a state-of-the-art baseline.

Related papers

• Conversational Question Answering with Reformulations over Knowledge Graph [48.581924841203325]
  State-of-the-art methods of ConvQA often struggle with inexplicit question-answer pairs. We propose a reinforcement learning based model, CornNet, to improve ConvQA performance. CornNet learns question representations using human writing reformulations, and a student model to mimic the teacher model's output.
  arXiv  Detail & Related papers  (2023-12-27T00:03:05Z)
• Open-Set Knowledge-Based Visual Question Answering with Inference Paths [79.55742631375063]
  The purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases. We propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity) Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process.
  arXiv  Detail & Related papers  (2023-10-12T09:12:50Z)
• Answering Ambiguous Questions via Iterative Prompting [84.3426020642704]
  In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. One approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. We present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions.
  arXiv  Detail & Related papers  (2023-07-08T04:32:17Z)
• Answering Unanswered Questions through Semantic Reformulations in Spoken QA [20.216161323866867]
  Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. We analyze failed QA requests to identify core challenges: lexical gaps, proposition types, complex syntactic structure, and high specificity. We propose a Semantic Question Reformulation (SURF) model offering three linguistically-grounded operations (repair, syntactic reshaping, generalization) to rewrite questions to facilitate answering.
  arXiv  Detail & Related papers  (2023-05-27T07:19:27Z)
• Reinforced Question Rewriting for Conversational Question Answering [25.555372505026526]
  We develop a model to rewrite conversational questions into self-contained ones. It allows using existing single-turn QA systems to avoid training a CQA model from scratch. We propose using QA feedback to supervise the rewriting model with reinforcement learning.
  arXiv  Detail & Related papers  (2022-10-27T21:23:36Z)
• Multifaceted Improvements for Conversational Open-Domain Question Answering [54.913313912927045]
  We propose a framework with Multifaceted Improvements for Conversational open-domain Question Answering (MICQA) Firstly, the proposed KL-divergence based regularization is able to lead to a better question understanding for retrieval and answer reading. Second, the added post-ranker module can push more relevant passages to the top placements and be selected for reader with a two-aspect constrains. Third, the well designed curriculum learning strategy effectively narrows the gap between the golden passage settings of training and inference, and encourages the reader to find true answer without the golden passage assistance.
  arXiv  Detail & Related papers  (2022-04-01T07:54:27Z)
• BERT-CoQAC: BERT-based Conversational Question Answering in Context [10.811729691130349]
  We introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system. Experiment results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC leader board.
  arXiv  Detail & Related papers  (2021-04-23T03:05:17Z)
• Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning [69.42679922160684]
  We propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.
  arXiv  Detail & Related papers  (2020-11-01T19:50:34Z)
• Towards Data Distillation for End-to-end Spoken Conversational Question Answering [65.124088336738]
  We propose a new Spoken Conversational Question Answering task (SCQA) SCQA aims at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. Our main objective is to build a QA system to deal with conversational questions both in spoken and text forms.
  arXiv  Detail & Related papers  (2020-10-18T05:53:39Z)
• A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering [15.355557454305776]
  We show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon. We present the results of this analysis on the TREC CAsT and QuAC (CANARD) datasets.
  arXiv  Detail & Related papers  (2020-10-13T06:29:51Z)

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.