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 with no code
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
An important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents.
DEIM: An effective deep encoding and interaction model for sentence matching
To solve this problem, we propose a sentence matching method based on deep encoding and interaction to extract deep semantic information.
Question-Answer Sentence Graph for Joint Modeling Answer Selection
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems.
Double Retrieval and Ranking for Accurate Question Answering
Recent work has shown that an answer verification step introduced in Transformer-based answer selection models can significantly improve the state of the art in Question Answering.
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data
Search engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages.
PerCQA: Persian Community Question Answering Dataset
In this paper, we present PerCQA, the first Persian dataset for CQA.
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.
If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering
We specifically emphasize on the importance of retrieving evidence jointly by showing several comparative analyses to other methods that retrieve and rerank evidence sentences individually.
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention.
A Multi-Size Neural Network with Attention Mechanism for Answer Selection
The experimental results show that (1) multi-size neural network (MSNN) is a more useful method to capture abstract features on different levels of granularities than single/multi-layer CNNs; (2) the attention mechanism (AM) is a better strategy to derive more informative representations; (3) AM-MSNN is a better architecture for the answer selection task for the moment.
