| Challenge: | Existing question-answering systems focus on answering individual questions, assuming they are devoid of context. |
| Approach: | They propose to ask multiple related questions in a dataset that includes human-authored questions. |
| Outcome: | The proposed system can answer human-authored questions better than existing systems. |
Similar Papers
Can You Unpack That? Learning to Rewrite Questions-in-Context (D19-1)
Copied to clipboard
| Challenge: | Existing QA datasets lack key NLP problems like coreference and ellipsis resolution. |
| Approach: | They propose a task of question-in-context rewriting to rewrite a context-dependent question into a self-contained question with the same answer. |
| Outcome: | The proposed task is based on a dataset of 40,527 questions based in QuAC . it requires models to link questions together to resolve conversational dependencies . |
Open-Domain Question Answering (2020.acl-tutorials)
Copied to clipboard
| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
| Approach: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering . focus will shift to cutting- edge models proposed for open- domain QA . |
| Outcome: | The tutorial will cover cutting-edge research in open-domain question answering (QA) it will cover two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever free methods . |
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
Copied to clipboard
| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References (2022.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to pretrain open-domain question answering systems lack task-specific annotations. |
| Approach: | They propose to pretrain a two-stage open-domain question answering system with strong transfer capabilities by using a dictionary and a large-scale corpus. |
| Outcome: | The proposed approach leads to 2%-10% gains in top-20 accuracy and improves with reader. |
QuAC: Question Answering in Context (D18-1)
Copied to clipboard
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
| Challenge: | a dataset for Question Answering in Context contains 14K information-seeking QA dialogs . questions are often more open-ended, unanswerable, or only meaningful within the dialog context . |
| Approach: | They propose a dataset for Question Answering in Context that contains 14K dialogs . they use a student to ask questions about a Wikipedia section and a teacher to answer them . |
| Outcome: | The proposed dataset underperforms humans in a number of reference models . the dataset contains 14K information-seeking dialogs over sections from Wikipedia . |
IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing open-domain QA tasks focus on questions whose answer can be deduced directly from global factual knowledge. |
| Approach: | They propose a dataset where each question is based on a counterfactual presupposition via an "if" clause. |
| Outcome: | The IfQA dataset contains 3,800 questions that were annotated by crowdworkers on relevant Wikipedia passages. |
DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (2024.findings-acl)
Copied to clipboard
| Challenge: | Question answering (QA) is a fundamental task in the field of Natural Language Processing (NLP). |
| Approach: | They propose a database querying and reasoning dataset for question answering that is designed to accommodate sequential questions and multi-hop queries. |
| Outcome: | The proposed dataset better mirrors the dynamics of real-world information retrieval and analysis with a particular focus on the financial reports of US companies. |
Question Answering in the Biomedical Domain (P19-2)
Copied to clipboard
| Challenge: | False positive questions require specific knowledge, common sense or a procedure due to ambiguity or the scope of the question. |
| Approach: | False q is a question answering technique that uses natural language to find an answer . Falsity is based on a lexical gap and quality of answer spans . |
| Outcome: | Using the proposed system, patients can self-diagnose without sacrificing quality of answer spans. |
Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are showing emerging abilities, but they are not large enough to assess their capabilities. |
| Approach: | They propose a benchmark that compares large language models with open and closed source models. |
| Outcome: | The proposed benchmark compares open and closed-source models with open-source and closed source models. |
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets (2021.eacl-main)
Copied to clipboard
| Challenge: | a recent study examines the ability of Open-Domain Question Answering models to produce answers to factoid questions . a large number of models have been used to study the performance of open-domain QA datasets . |
| Approach: | They evaluate open-domain question answering models to see what they can generalize . they find that all models perform substantially worse on questions that cannot be memorized from train sets . |
| Outcome: | The proposed model outperforms a closed-book QA model on the open-domain datasets. |