Answer Selection
50 papers with code • 6 benchmarks • 10 datasets
Answer Selection is the task of identifying the correct answer to a question from a pool of candidate answers. This task can be formulated as a classification or a ranking problem.
Source: Learning Analogy-Preserving Sentence Embeddings for Answer Selection
Benchmarks
These leaderboards are used to track progress in Answer Selection
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WikiQA
TrecQA
UDC
InsuranceQA
CICERO
SelQA
Most implemented papers
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
(ii) We propose three attention schemes that integrate mutual influence between sentences into CNN; thus, the representation of each sentence takes into consideration its counterpart.
Neural Variational Inference for Text Processing
carpedm20/variational-text-tensorflow
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We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.
Gated-Attention Readers for Text Comprehension
In this paper we study the problem of answering cloze-style questions over documents.
Attentive Pooling Networks
winter1997/Attentive-Pooling-Networks
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In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training.
Learning to Rank Question-Answer Pairs using Hierarchical Recurrent Encoder with Latent Topic Clustering
david-yoon/QA_HRDE_LTC
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NAACL 2018
In this paper, we propose a novel end-to-end neural architecture for ranking candidate answers, that adapts a hierarchical recurrent neural network and a latent topic clustering module.
Simple and Effective Text Matching with Richer Alignment Features
hitvoice/RE2
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ACL 2019
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
Applying Deep Learning to Answer Selection: A Study and An Open Task
We apply a general deep learning framework to address the non-factoid question answering task.
LSTM-based Deep Learning Models for Non-factoid Answer Selection
One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.
Learning Recurrent Span Representations for Extractive Question Answering
In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network.
