Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs

• URL: http://arxiv.org/abs/2310.07008v1
• Date: Tue, 10 Oct 2023 20:49:43 GMT
• Title: Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs
• Authors: Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski, Anton Razzhigaev, Valentin Malykh, Alexander Panchenko
• Abstract summary: We present a novel approach which works on top of the pre-trained Text-to-Text QA system to address this issue. Our simple yet effective method performs filtering and re-ranking of generated candidates based on their types derived from Wikidata "instance_of" property.
• Score: 62.20354845651949
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the models is limited and the quality decreases for questions with less popular entities. In this paper, we present a novel approach which works on top of the pre-trained Text-to-Text QA system to address this issue. Our simple yet effective method performs filtering and re-ranking of generated candidates based on their types derived from Wikidata "instance_of" property.

Related papers

• Harnessing the Power of Prompt-based Techniques for Generating School-Level Questions using Large Language Models [0.5459032912385802]
  We propose a novel approach that utilizes prompt-based techniques to generate descriptive and reasoning-based questions. We curate a new QG dataset called EduProbe for school-level subjects, by leveraging the rich content of NCERT textbooks. We investigate several prompt-based QG methods by fine-tuning transformer-based large language models.
  arXiv  Detail & Related papers  (2023-12-02T05:13:28Z)
• Large Language Models Meet Knowledge Graphs to Answer Factoid Questions [57.47634017738877]
  We propose a method for exploring pre-trained Text-to-Text Language Models enriched with additional information from Knowledge Graphs. We procure easily interpreted information with Transformer-based models through the linearization of the extracted subgraphs. Final re-ranking of the answer candidates with the extracted information boosts Hits@1 scores of the pre-trained text-to-text language models by 4-6%.
  arXiv  Detail & Related papers  (2023-10-03T15:57:00Z)
• A Practical Toolkit for Multilingual Question and Answer Generation [79.31199020420827]
  We introduce AutoQG, an online service for multilingual QAG, along with lmqg, an all-in-one Python package for model fine-tuning, generation, and evaluation. We also release QAG models in eight languages fine-tuned on a few variants of pre-trained encoder-decoder language models, which can be used online via AutoQG or locally via lmqg.
  arXiv  Detail & Related papers  (2023-05-27T08:42:37Z)
• PAXQA: Generating Cross-lingual Question Answering Examples at Training Scale [53.92008514395125]
  PAXQA (Projecting annotations for cross-lingual (x) QA) decomposes cross-lingual QA into two stages. We propose a novel use of lexically-constrained machine translation, in which constrained entities are extracted from the parallel bitexts. We show that models fine-tuned on these datasets outperform prior synthetic data generation models over several extractive QA datasets.
  arXiv  Detail & Related papers  (2023-04-24T15:46:26Z)
• Automatic Short Math Answer Grading via In-context Meta-learning [2.0263791972068628]
  We study the problem of automatic short answer grading for students' responses to math questions. We use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model. Second, we use an in-context learning approach that provides scoring examples as input to the language model.
  arXiv  Detail & Related papers  (2022-05-30T16:26:02Z)
• Self-Supervised VQA: Answering Visual Questions using Images and Captions [38.05223339919346]
  VQA models assume the availability of datasets with human-annotated Image-Question-Answer(I-Q-A) triplets for training. We study whether models can be trained without any human-annotated Q-A pairs, but only with images and associated text captions.
  arXiv  Detail & Related papers  (2020-12-04T01:22:05Z)
• 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)
• FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT [0.5772546394254112]
  We develop a model based on BERT, a state-of-the-art transformer network. We are ranked first in the leader board with test accuracy of 87.79%.
  arXiv  Detail & Related papers  (2020-08-22T08:04:21Z)
• 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)
• Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering [98.48363619128108]
  We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
  arXiv  Detail & Related papers  (2020-04-24T17:57:45Z)

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.