| Challenge: | Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension. |
| Approach: | They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query. |
| Outcome: | The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes. |
Similar Papers
Contextualized Word Representations for Reading Comprehension (N18-2)
Copied to clipboard
| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
| Outcome: | The proposed model improves on the competitive SQuAD dataset by providing rich contextualized word representations and allowing it to choose between context-dependent and context-independent representations. |
Integrating Question Classification and Deep Learning for improved Answer Selection (C18-1)
Copied to clipboard
| Challenge: | Question Answering (QA) is the task of automatically generating answers to questions posed in natural language. |
| Approach: | They propose a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer selection. |
| Outcome: | The proposed system outperforms the current state of the art in all variations except one . the proposed system improves QA by reducing the search space of potential answers . |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to QA re-rank sentences use huge neural models or complex attentive architectures. |
| Approach: | They propose to exploit the intrinsic structure of the original rank with an effective word-relatedness encoder to achieve the highest accuracy among the cost-efficient models. |
| Outcome: | The proposed model takes 9.5 seconds to train on the WikiQA dataset, compared with 18 minutes required by a standard BERT-base fine-tuning. |
A Review on Deep Learning Techniques Applied to Answer Selection (C18-1)
Copied to clipboard
| Challenge: | Existing deep learning methods for answer selection are not feature engineering or expensive external resources. |
| Approach: | They propose to use deep learning methods to analyze and predict answer quality . they use a set of candidate answers to identify which of the candidates answers the question correctly. |
| Outcome: | The proposed methods produce impressive performance without feature engineering or expensive external resources. |
Double Retrieval and Ranking for Accurate Question Answering (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent work shows that answer verification models can improve the state of the art in Question Answering . despite the fact that the supporting candidates are ranked only according to the relevancy with the question, the model still lacks the support needed for other answer candidates. |
| Approach: | They propose a double reranking model that selects the best support for each target answer . they propose 'second neural retrieval stage' to encode question and answer pair as query . |
| Outcome: | The proposed approach improves the state of the art in Question Answering . the proposed model ranked candidates according to relevancy and not the answer . but the proposed approach fails to provide the best support . |
ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)
Copied to clipboard
| Challenge: | Popular QA benchmarks like SQuAD have driven progress on identifying answer spans within a specific passage . retrieving relevant answers from a huge corpus of documents is still a challenging problem . |
| Approach: | They propose a benchmark for evaluating large-scale sentence-level answer retrieval models . they establish baselines using both neural encoding models and classical retrieval techniques . |
| Outcome: | The proposed model outperforms human models on identifying answer spans within a specific passage . the proposed model is scalable and can bypass the typical document retrieval step . |
Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
Copied to clipboard
| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
Retrieving Support to Rank Answers in Open-Domain Question Answering (2025.emnlp-main)
Copied to clipboard
| Challenge: | a novel question answering architecture retrieves content relevant to the combined pair . previous work on automatic claim verification has shown hallucinations . |
| Approach: | They propose a question-answer architecture that prioritizes supporting evidence . it retrieves paragraphs that directly substantiate the correctness of a with respect to q . |
| Outcome: | The proposed approach can be used by large language models to retrieve explanatory paragraphs that ground their reasoning. |
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems (2022.naacl-industry)
Copied to clipboard
| Challenge: | a recent study shows that text retrieval can be used to find information relevant to user requests. |
| Approach: | They propose to use a corpus of text to search for relevant responses to user requests . they compare this approach to other methods that use intent detection . |
| Outcome: | a new approach can be used to search through a corpus of text to find relevant responses to user requests. |