Attention-based model for predicting question relatedness on Stack Overflow

• URL: http://arxiv.org/abs/2103.10763v2
• Date: Mon, 22 Mar 2021 09:12:02 GMT
• Title: Attention-based model for predicting question relatedness on Stack Overflow
• Authors: Jiayan Pei, Yimin wu, Zishan Qin, Yao Cong, Jingtao Guan
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stack Overflow is one of the most popular Programming Community-based Question Answering (PCQA) websites that has attracted more and more users in recent years. When users raise or inquire questions in Stack Overflow, providing related questions can help them solve problems. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited since interaction information between two questions may be lost. In this paper, we adopt the deep learning technique, propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically. We adopt the attention mechanism to capture the semantic interaction information between the questions. Besides, we have pre-trained and released word embeddings specific to the software engineering domain for this task, which may also help other related tasks. The experiment results demonstrate that ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics, achieving state-of-the-art performance in this task. Our model also performs well in the duplicate question detection task of AskUbuntu, which is a similar but different task, proving its generalization and robustness.

Related papers

• Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms [1.8749305679160364]
  We propose a tool that can surface near-duplicate and semantically related questions without supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches. We demonstrate that QDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed.
  arXiv  Detail & Related papers  (2022-12-20T11:52:52Z)
• Best-Answer Prediction in Q&A Sites Using User Information [2.982218441172364]
  Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features.
  arXiv  Detail & Related papers  (2022-12-15T02:28:52Z)
• 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)
• Modern Question Answering Datasets and Benchmarks: A Survey [5.026863544662493]
  Question Answering (QA) is one of the most important natural language processing (NLP) tasks. It aims using NLP technologies to generate a corresponding answer to a given question based on the massive unstructured corpus. In this paper, we investigate influential QA datasets that have been released in the era of deep learning.
  arXiv  Detail & Related papers  (2022-06-30T05:53:56Z)
• 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)
• Learning with Instance Bundles for Reading Comprehension [61.823444215188296]
  We introduce new supervision techniques that compare question-answer scores across multiple related instances. Specifically, we normalize these scores across various neighborhoods of closely contrasting questions and/or answers. We empirically demonstrate the effectiveness of training with instance bundles on two datasets.
  arXiv  Detail & Related papers  (2021-04-18T06:17:54Z)
• Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning [55.08037694027792]
  Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB) The conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
  arXiv  Detail & Related papers  (2020-10-29T18:34:55Z)
• Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning [56.771557756836906]
  We present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases.
  arXiv  Detail & Related papers  (2020-10-29T18:28:16Z)
• Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection [10.82418428209551]
  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.
  arXiv  Detail & Related papers  (2020-10-27T05:03:34Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)

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.