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
Latest papers
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors
fudanvi/generative-abstract-reasoning
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In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables.
[Re] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
jishnujayakumar/MLRC2020-EmbedKGQA
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RC 2020
In addition to making the codebase more modular and easy to navigate, we have made changes to incorporate different transformers in the question embedding module.
ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks
benywon/ComQA
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In compositional question answering, the systems should assemble several supporting evidence from the document to generate the final answer, which is more difficult than sentence-level or phrase-level QA.
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
whalefallzz/nut_rc
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COLING 2020
Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.
Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection
tahmedge/BERT-for-Answer-Selection
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We find that fine-tuning the BERT model for the answer selection task is very effective and observe a maximum improvement of 13. 1% in the QA datasets and 18. 7% in the CQA datasets compared to the previous state-of-the-art.
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering
The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area.
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
ukplab/emnlp2020-multicqa
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EMNLP 2020
We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines.
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
malllabiisc/EmbedKGQA
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ACL 2020
In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction.
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task
tahmedge/CETE-LREC
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LREC 2020
In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task.
Review-guided Helpful Answer Identification in E-commerce
isakzhang/answer-helpfulness-prediction
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In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.
