Summary Answering Ambiguous Questions with a Database arxiv.org
6,666 words - PDF document - View PDF document
One Line
Developing virtual knowledge bases is a solution to address the challenge of answering ambiguous questions in open-domain question answering.
Slides
Slide Presentation (12 slides)
Key Points
- Answering ambiguous questions is a challenging task in open-domain question answering.
- Current state-of-the-art method uses a database of unambiguous questions generated from Wikipedia to improve performance.
- Virtual knowledge bases have been proposed to address the limitations of traditional knowledge bases.
- A three-stage process is described for answering ambiguous questions using a database.
- Retrieval models like BM25 and GTR are used to encode and retrieve questions from the database.
- Question revisions are generated by moving information from passages to questions.
- DPR and GTR-large are popular retrieval methods for question-based and passage retrieval.
- The effectiveness of using a database to answer ambiguous questions is discussed, along with approaches to increase information coverage.
Summaries
31 word summary
Answering ambiguous questions in open-domain question answering is challenging. The current state-of-the-art method uses a database of unambiguous questions from Wikipedia. Virtual knowledge bases have been developed to address this issue.
38 word summary
Answering ambiguous questions in open-domain question answering is challenging. The current state-of-the-art method uses a database of unambiguous questions from Wikipedia. However, knowledge bases may not align with users' questions. To address this, virtual knowledge bases have been
369 word summary
Answering ambiguous questions is a challenging task in open-domain question answering. Many questions have multiple possible answers depending on the interpretation. The current state-of-the-art method utilizes a database of unambiguous questions generated from Wikipedia to improve performance. This approach shows a
Knowledge bases are commonly used in knowledge-intensive tasks, but they lack the ability to represent complex relationships and may not align with users' questions. To address this, virtual knowledge bases have been proposed that are not restricted to predefined vocabularies. These virtual
In this document, the authors describe a three-stage process for answering ambiguous questions using a database. In the second stage, they merge spans that are identical after removing articles and punctuation, resulting in 283 million answers from 21 million Wikipedia passages.
Retrieving questions from a database is similar to retrieving passages from a text corpus. The paper uses two retrieval models, BM25 and GTR, to encode and retrieve questions. Questions from SIXPAQ are then mapped to the passages they were generated from
Table 1 shows examples of question revisions in the context of answering ambiguous questions. The revised questions are generated by moving information from passages to questions. The model used for this task is a T5-large model. The revision process is repeated multiple times to
DPR and GTR-large are popular retrieval methods used for question-based retrieval and passage retrieval. In terms of passage-based retrieval, indirect retrieval with SIXPAQ outperforms BM25 and GTR, resulting in improved recall@10 scores on Amb
The performance of long-form answer generation using retrieved answers and passages was evaluated. The results showed that as the number of passages increased, both STR-EM and DISAMBIG-F1 decreased. The addition of questions from SIXPAQ to the input improved
The document discusses the effectiveness of using a database to answer ambiguous questions. The authors compare different approaches to increase information coverage and show that retrieving from generated questions can improve the diversity of retrieval results. They also demonstrate that the increase in recall, combined with concise
The summary provides a list of references to academic papers and preprints related to answering ambiguous questions with a database. The papers cover various topics such as open-domain question answering, passage ranking, transfer learning, machine comprehension of text, factoid questions, conditional