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Found 14 papers, 2 papers with code
Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection
In this paper, we propose to improve the candidate scoring by explicitly incorporating the dependencies between question-context and answer-context into the final representation of a candidate.
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection
While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-filling, question-answering (QA), fact verification, and entity-linking tasks.
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.
In Situ Answer Sentence Selection at Web-scale
Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i. e., sentences, extracted from the retrieved text.
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.
Joint Models for Answer Verification in Question Answering Systems
This paper studies joint models for selecting correct answer sentences among the top $k$ provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems.
AVA: an Automatic eValuation Approach for Question Answering Systems
We introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers (references), can estimate system Accuracy.
Reference-based Weak Supervision for Answer Sentence Selection using Web Data
Answer sentence selection (AS2) modeling requires annotated data, i. e., hand-labeled question-answer pairs.
Multilingual Answer Sentence Reranking via Automatically Translated Data
We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems.
CDA: a Cost Efficient Content-based Multilingual Web Document Aligner
We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level.
Machine Translation Customization via Automatic Training Data Selection from the Web
Machine translation (MT) systems, especially when designed for an industrial setting, are trained with general parallel data derived from the Web.
AVA: an Automatic eValuation Approach to Question Answering Systems
This allows for effectively measuring the similarity between the reference and an automatic answer, biased towards the question semantics.
TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection
Additionally, we show that the transfer step of TANDA makes the adaptation step more robust to noise.
Ranked #2 on
Question Answering
on TrecQA
(using extra training data)
