Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection

• URL: http://arxiv.org/abs/2010.14049v1
• Date: Tue, 27 Oct 2020 05:03:34 GMT
• Title: Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection
• Authors: Wen-Ting Tseng, Tien-Hong Lo, Yung-Chang Hsu and Berlin Chen
• Abstract summary: We propose a contextual language model for retrieving appropriate answers to frequently asked questions. We also explore to capitalize on domain-specific topically-relevant relations between words in an unsupervised manner. We evaluate variants of our approach on a publicly-available Chinese FAQ dataset, and further apply and contextualize it to a large-scale question-matching task.
• Score: 10.82418428209551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frequently asked question (FAQ) retrieval, with the purpose of providing information on frequent questions or concerns, has far-reaching applications in many areas, where a collection of question-answer (Q-A) pairs compiled a priori can be employed to retrieve an appropriate answer in response to a user\u2019s query that is likely to reoccur frequently. To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure. In this paper, we extend this line of research by combining the clues gathered from the q-Q similarity measure and the q-A relevance measure and meanwhile injecting extra word interaction information, distilled from a generic (open domain) knowledge base, into a contextual language model for inferring the q-A relevance. Furthermore, we also explore to capitalize on domain-specific topically-relevant relations between words in an unsupervised manner, acting as a surrogate to the supervised domain-specific knowledge base information. As such, it enables the model to equip sentence representations with the knowledge about domain-specific and topically-relevant relations among words, thereby providing a better q-A relevance measure. We evaluate variants of our approach on a publicly-available Chinese FAQ dataset, and further apply and contextualize it to a large-scale question-matching task, which aims to search questions from a QA dataset that have a similar intent as an input query. Extensive experimental results on these two datasets confirm the promising performance of the proposed approach in relation to some state-of-the-art ones.

Related papers

• Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check [25.63538452425097]
  We propose a conversation-level RAG approach, which incorporates fine-grained retrieval augmentation and self-check for conversational question answering. In particular, our approach consists of three components, namely conversational question refiner, fine-grained retriever and self-check based response generator.
  arXiv  Detail & Related papers  (2024-03-27T04:20:18Z)
• MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval [1.7403133838762446]
  We propose a bi-encoder-based query-FAQ matching model that leverages multiple combinations of FAQ fields. Our model achieves around 27% and 20% better top-1 accuracy for the FAQ retrieval task on internal and open datasets.
  arXiv  Detail & Related papers  (2023-02-23T12:02:49Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases [67.33560134350427]
  TempQA-WD is a benchmark dataset for temporal reasoning. It is based on Wikidata, which is the most frequently curated, openly available knowledge base.
  arXiv  Detail & Related papers  (2022-01-15T08:49:09Z)
• Relation-Guided Pre-Training for Open-Domain Question Answering [67.86958978322188]
  We propose a Relation-Guided Pre-Training (RGPT-QA) framework to solve complex open-domain questions. We show that RGPT-QA achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions.
  arXiv  Detail & Related papers  (2021-09-21T17:59:31Z)
• SituatedQA: Incorporating Extra-Linguistic Contexts into QA [7.495151447459443]
  We introduce SituatedQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context. We find that a significant proportion of information seeking questions have context-dependent answers. Our study shows that existing models struggle with producing answers that are frequently updated or from uncommon locations.
  arXiv  Detail & Related papers  (2021-09-13T17:53:21Z)
• Attention-based model for predicting question relatedness on Stack Overflow [0.0]
  We propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically. ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics. Our model also performs well in the duplicate question detection task of Ask Ubuntu.
  arXiv  Detail & Related papers  (2021-03-19T12:18:03Z)
• 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)
• Answering Any-hop Open-domain Questions with Iterative Document Reranking [62.76025579681472]
  We propose a unified QA framework to answer any-hop open-domain questions. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets.
  arXiv  Detail & Related papers  (2020-09-16T04:31:38Z)
• 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.