196,075 research outputs found
1-PAGER: One Pass Answer Generation and Evidence Retrieval
We present 1-Pager the first system that answers a question and retrieves
evidence using a single Transformer-based model and decoding process. 1-Pager
incrementally partitions the retrieval corpus using constrained decoding to
select a document and answer string, and we show that this is competitive with
comparable retrieve-and-read alternatives according to both retrieval and
answer accuracy metrics. 1-Pager also outperforms the equivalent closed-book
question answering model, by grounding predictions in an evidence corpus. While
1-Pager is not yet on-par with more expensive systems that read many more
documents before generating an answer, we argue that it provides an important
step toward attributed generation by folding retrieval into the
sequence-to-sequence paradigm that is currently dominant in NLP. We also show
that the search paths used to partition the corpus are easy to read and
understand, paving a way forward for interpretable neural retrieval.Comment: Accepted at EMNLP 2023 (Findings
Query-based extracting: how to support the answer?
Human-made query-based summaries commonly contain information not explicitly asked for. They answer the user query, but also provide supporting information. In order to find this information in the source text, a graph is used to model the strength and type of relations between sentences of the query and document cluster, based on various features. The resulting extracts rank second in overall readability in the DUC 2006 evaluation. Employment of better question answering methods is the key to improve also content-based evaluation results
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during training, and use a shared-normalization training objective
that encourages the model to produce globally correct output. We combine this
method with a state-of-the-art pipeline for training models on document QA
data. Experiments demonstrate strong performance on several document QA
datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion
of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.Comment: 11 pages, updated a referenc
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during training, and use a shared-normalization training objective
that encourages the model to produce globally correct output. We combine this
method with a state-of-the-art pipeline for training models on document QA
data. Experiments demonstrate strong performance on several document QA
datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion
of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.Comment: 11 pages, updated a referenc
Using ontology in query answering systems: Scenarios, requirements and challenges
Equipped with the ultimate query answering system, computers would finally be in a position to address all our information needs in a natural way. In this paper, we describe how Language and Computing nv (L&C), a developer of ontology-based natural language understanding systems for the healthcare domain, is working towards the ultimate Question Answering (QA) System for healthcare workers. L&Cās company strategy in this area is to design in a step-by-step fashion the essential components of such a system, each component being designed to solve some one part of the total problem and at the same time reflect well-defined needs on the prat of our customers. We compare our strategy with the research roadmap proposed by the Question Answering Committee of the National Institute of Standards and Technology (NIST), paying special attention to the role of ontology
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