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Found 51 papers, 12 papers with code
Building a Dataset for Automatically Learning to Detect Questions Requiring Clarification
Question Answering (QA) systems aim to return correct and concise answers in response to user questions.
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions.
Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages
While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets.
Learning Answer Generation using Supervision from Automatic Question Answering Evaluators
Recent studies show that sentence-level extractive QA, i. e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers using the top-k answer sentences ranked by AS2 models (a la retrieval-augmented generation style).
Context-Aware Transformer Pre-Training for Answer Sentence Selection
Answer Sentence Selection (AS2) is a core component for building an accurate Question Answering pipeline.
Ranked #4 on
Question Answering
on TrecQA
(using extra training data)
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval
For this purpose, we (i) build a large scale DB of 6. 3M q/a pairs, using public questions, (ii) design a new system based on neural IR and a q/a pair reranker, and (iii) construct training and test data to perform comparative experiments with our models.
Effective Pre-Training Objectives for Transformer-based Autoencoders
In this paper, we study trade-offs between efficiency, cost and accuracy when pre-training Transformer encoders with different pre-training objectives.
Knowledge Transfer from Answer Ranking to Answer Generation
In this paper, we propose to train a GenQA model by transferring knowledge from a trained AS2 model, to overcome the aforementioned issue.
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.
Ranked #1 on
Answer Selection
on ASNQ
Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Our evaluation on three AS2 and one fact verification datasets demonstrates the superiority of our pre-training technique over the traditional ones for transformers used as joint models for multi-candidate inference tasks, as well as when used as cross-encoders for sentence-pair formulations of these tasks.
Ranked #3 on
Fact Verification
on FEVER
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.
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members.
Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation
Our cross-lingual generative system outperforms answer sentence selection baselines for all 5 languages and monolingual generative pipelines for three out of five languages studied.
Will this Question be Answered? Question Filtering via Answer Model Distillation for Efficient Question Answering
In this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them.
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation
Recent works made significant advances on summarization tasks, facilitated by summarization datasets.
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.
Answer Generation for Retrieval-based Question Answering Systems
Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results.
Supervised Neural Clustering via Latent Structured Output Learning: Application to Question Intents
Previous pre-neural work on structured prediction has produced very effective supervised clustering algorithms using linear classifiers, e. g., structured SVM or perceptron.
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.
Efficient pre-training objectives for Transformers
The Transformer architecture deeply changed the natural language processing, outperforming all previous state-of-the-art models.
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.
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.
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.
Modeling Context in Answer Sentence Selection Systems on a Latency Budget
In this work, we present an approach to efficiently incorporate contextual information in AS2 models.
Machine Reading Comprehension
Open-Domain Question Answering
+1
Context-based Transformer Models for Answer Sentence Selection
An important task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked question.
The Cascade Transformer: an Application for Efficient Answer Sentence Selection
Large transformer-based language models have been shown to be very effective in many classification tasks.
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.
A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection
An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i. e., Answer Sentence Selection (A2S).
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
SemEval-2017 Task 3: Community Question Answering
We describe SemEval-2017 Task 3 on Community Question Answering.
SemEval-2015 Task 3: Answer Selection in Community Question Answering
Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e. g., the exploitation of the interaction between users and the structure of related posts.
Global Thread-Level Inference for Comment Classification in Community Question Answering
Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd.
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)
Transfer Learning for Sequence Labeling Using Source Model and Target Data
In this paper, we propose an approach for transferring the knowledge of a neural model for sequence labeling, learned from the source domain, to a new model trained on a target domain, where new label categories appear.
Adversarial Domain Adaptation for Duplicate Question Detection
We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions.
Injecting Relational Structural Representation in Neural Networks for Question Similarity
Effectively using full syntactic parsing information in Neural Networks (NNs) to solve relational tasks, e. g., question similarity, is still an open problem.
Integrating Stance Detection and Fact Checking in a Unified Corpus
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e. g., news websites, social media, etc.
Automatic Stance Detection Using End-to-End Memory Networks
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction.
Ranked #6 on
Fake News Detection
on FNC-1
Cross-Language Question Re-Ranking
We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings.
Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics
Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbots.
Multitask Learning with Deep Neural Networks for Community Question Answering
In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i. e., question-comment similarity, question-question similarity and new question-comment similarity.
Addressing Community Question Answering in English and Arabic
In the case of the Arabic question re-ranking task, for the first time we applied tree kernels on syntactic trees of Arabic sentences.
Modeling Relational Information in Question-Answer Pairs with Convolutional Neural Networks
In this paper, we propose convolutional neural networks for learning an optimal representation of question and answer sentences.
Semi-supervised Question Retrieval with Gated Convolutions
Question answering forums are rapidly growing in size with no effective automated ability to refer to and reuse answers already available for previous posted questions.
