Answer Selection
47 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.
A Compare-Aggregate Model for Matching Text Sequences
We particularly focus on the different comparison functions we can use to match two vectors.
