A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling

• URL: http://arxiv.org/abs/2311.15513v1
• Date: Mon, 27 Nov 2023 03:17:09 GMT
• Title: A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling
• Authors: Shashidhar Reddy Javaji, Haoran Hu, Sai Sameer Vennam, Vijaya Gajanan Buddhavarapu
• Abstract summary: Question answer generation is highly relevant because a frequently asked questions (FAQ) list can only have a finite amount of questions. A model which can perform question answer generation could be able to answer completely new questions that are within the scope of the data. In commercial applications, it can be used to increase customer satisfaction and ease of usage. However a lot of data is generated by humans so it is susceptible to human error and this can adversely affect the model's performance.
• Score: 0.49157446832511503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answer generation using Natural Language Processing models is ubiquitous in the world around us. It is used in many use cases such as the building of chat bots, suggestive prompts in google search and also as a way of navigating information in banking mobile applications etc. It is highly relevant because a frequently asked questions (FAQ) list can only have a finite amount of questions but a model which can perform question answer generation could be able to answer completely new questions that are within the scope of the data. This helps us to be able to answer new questions accurately as long as it is a relevant question. In commercial applications, it can be used to increase customer satisfaction and ease of usage. However a lot of data is generated by humans so it is susceptible to human error and this can adversely affect the model's performance and we are investigating this through our work

Related papers

• Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries [91.70689724416698]
  We present NatQuest, a collection of 13,500 naturally occurring questions from three diverse sources. Our analysis reveals a significant presence of causal questions (up to 42%) within the dataset.
  arXiv  Detail & Related papers  (2024-05-30T17:55:28Z)
• STaR-GATE: Teaching Language Models to Ask Clarifying Questions [32.71841885198304]
  We explore a language model's ability to self-improve by rewarding it for generating useful questions. We generate a synthetic dataset of 25,500 unique persona-task prompts. We find that teaching a language model to ask better questions leads to better personalized responses.
  arXiv  Detail & Related papers  (2024-03-28T05:35:22Z)
• Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions [95.92276099234344]
  We present a new state-of-the-art for answering ambiguous questions that exploits a database of unambiguous questions generated from Wikipedia. Our method improves performance by 15% on recall measures and 10% on measures which evaluate disambiguating questions from predicted outputs.
  arXiv  Detail & Related papers  (2023-08-16T20:23:16Z)
• MixQG: Neural Question Generation with Mixed Answer Types [54.23205265351248]
  We propose a neural question generator, MixQG, to bridge this gap. We combine 9 question answering datasets with diverse answer types, including yes/no, multiple-choice, extractive, and abstractive answers. Our model outperforms existing work in both seen and unseen domains.
  arXiv  Detail & Related papers  (2021-10-15T16:03:40Z)
• Generating Answer Candidates for Quizzes and Answer-Aware Question Generators [16.44011627249311]
  We propose a model that can generate a specified number of answer candidates for a given passage of text. Our experiments show that our proposed answer candidate generation model outperforms several baselines.
  arXiv  Detail & Related papers  (2021-08-29T19:33:51Z)
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers [66.11048565324468]
  We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
  arXiv  Detail & Related papers  (2021-05-07T00:12:34Z)
• 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)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)
• Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus [23.676748207014903]
  We propose Answer-Clue-Style-aware Question Generation (ACS-QG) It aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale. We can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
  arXiv  Detail & Related papers  (2020-01-27T05:27:09Z)

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.