ParaQA: A Question Answering Dataset with Paraphrase Responses for
Single-Turn Conversation
- URL: http://arxiv.org/abs/2103.07771v1
- Date: Sat, 13 Mar 2021 18:53:07 GMT
- Title: ParaQA: A Question Answering Dataset with Paraphrase Responses for
Single-Turn Conversation
- Authors: Endri Kacupaj, Barshana Banerjee, Kuldeep Singh, Jens Lehmann
- Abstract summary: ParaQA is a dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG)
The dataset was created using a semi-automated framework for generating diverse paraphrasing of the answers using techniques such as back-translation.
- Score: 5.087932295628364
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper presents ParaQA, a question answering (QA) dataset with multiple
paraphrased responses for single-turn conversation over knowledge graphs (KG).
The dataset was created using a semi-automated framework for generating diverse
paraphrasing of the answers using techniques such as back-translation. The
existing datasets for conversational question answering over KGs
(single-turn/multi-turn) focus on question paraphrasing and provide only up to
one answer verbalization. However, ParaQA contains 5000 question-answer pairs
with a minimum of two and a maximum of eight unique paraphrased responses for
each question. We complement the dataset with baseline models and illustrate
the advantage of having multiple paraphrased answers through commonly used
metrics such as BLEU and METEOR. The ParaQA dataset is publicly available on a
persistent URI for broader usage and adaptation in the research community.
Related papers
- PCoQA: Persian Conversational Question Answering Dataset [12.07607688189035]
The PCoQA dataset is a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions.
PCoQA is designed to present novel challenges compared to previous question answering datasets.
This paper not only presents the comprehensive PCoQA dataset but also reports the performance of various benchmark models.
arXiv Detail & Related papers (2023-12-07T15:29:34Z) - Semantic Parsing for Conversational Question Answering over Knowledge
Graphs [63.939700311269156]
We develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof.
We present two different semantic parsing approaches and highlight the challenges of the task.
Our dataset and models are released at https://github.com/Edinburgh/SPICE.
arXiv Detail & Related papers (2023-01-28T14:45:11Z) - Conversational QA Dataset Generation with Answer Revision [2.5838973036257458]
We introduce a novel framework that extracts question-worthy phrases from a passage and then generates corresponding questions considering previous conversations.
Our framework revises the extracted answers after generating questions so that answers exactly match paired questions.
arXiv Detail & Related papers (2022-09-23T04:05:38Z) - An Answer Verbalization Dataset for Conversational Question Answerings
over Knowledge Graphs [9.979689965471428]
This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with verbalized answers.
We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness.
arXiv Detail & Related papers (2022-08-13T21:21:28Z) - ConditionalQA: A Complex Reading Comprehension Dataset with Conditional
Answers [93.55268936974971]
We describe a Question Answering dataset that contains complex questions with conditional answers.
We call this dataset ConditionalQA.
We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions.
arXiv Detail & Related papers (2021-10-13T17:16:46Z) - TopiOCQA: Open-domain Conversational Question Answeringwith Topic
Switching [11.717296856448566]
We introduce TopiOCQA, an open-domain conversational dataset with topic switches on Wikipedia.
TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers.
We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models.
arXiv Detail & Related papers (2021-10-02T09:53:48Z) - QAConv: Question Answering on Informative Conversations [85.2923607672282]
We focus on informative conversations including business emails, panel discussions, and work channels.
In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions.
arXiv Detail & Related papers (2021-05-14T15:53:05Z) - GooAQ: Open Question Answering with Diverse Answer Types [63.06454855313667]
We present GooAQ, a large-scale dataset with a variety of answer types.
This dataset contains over 5 million questions and 3 million answers collected from Google.
arXiv Detail & Related papers (2021-04-18T05:40:39Z) - Open Question Answering over Tables and Text [55.8412170633547]
In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question.
Most open QA systems have considered only retrieving information from unstructured text.
We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
arXiv Detail & Related papers (2020-10-20T16:48:14Z) - Towards Data Distillation for End-to-end Spoken Conversational Question
Answering [65.124088336738]
We propose a new Spoken Conversational Question Answering task (SCQA)
SCQA aims at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora.
Our main objective is to build a QA system to deal with conversational questions both in spoken and text forms.
arXiv Detail & Related papers (2020-10-18T05:53:39Z) - Fluent Response Generation for Conversational Question Answering [15.826109118064716]
We propose a method for situating responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses.
We use data augmentation to generate training data for an end-to-end system.
arXiv Detail & Related papers (2020-05-21T04:57:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.