Open-World Factually Consistent Question Generation (2023)

Abstract: Question generation methods based on pre-trained language models often suffer from factual inconsistencies and incorrect entities and are not answerable from the input paragraph. Domain shift – where the test data is from a different domain than the training data - further exacerbates the problem of hallucination. This is a critical issue for any natural language application doing question generation. In this work, we propose an effective data processing technique based on de-lexicalization for consistent question generation across domains. Unlike existing approaches for remedying hallucination, the proposed approach does not filter training data and is generic across question-generation models. Experimental results across six benchmark datasets show that our model is robust to domain shift and produces entity-level factually consistent questions without significant impact on traditional metrics.

## introduction question generation is the task of generating a question that is relevant to and answerable by a piece of text @xcite , @xcite , @xcite , @xcite , ). it is an important task in language generation @xcite , @xcite ), education @xcite ), and information retrieval @xcite ). a critical metric for question generation is factual consistency, i.e., the question has facts that are derivable from the input paragraph. this work proposes novel methods to improve entitylevel factual consistency while agnostic to model and underlying training data. @xcite and @xcite solve a similar problem for summarization. however, to the best of our knowledge, no work addresses the issue of entitylevel factual inconsistency for question generation. nema and @xcite have shown that name entities are essential for a question's answerability. the presence of wrong entities may make the question nonsensical and unanswerable. unlike previous work in the summarization field @xcite , @xcite , @xcite ), our work is independent of the model or training process. we also do not reduce dataset size by filtering. instead, we preprocess datasets to force the model to generate questions faithful to the input using strategies of de-lexicalization and multi-generation and recommend the best strategy. the proposed method improves the factual consistency by 84-100% across multiple datasets while having minimal impact on traditional performance metrics. we experimented with two popular language models viz. pegasus-large and bart-large @xcite . our proposed approach consistently performs better for both the language models than normal finetuning. we also compare our approach to recent methods for addressing hallucination in summarization, and our results showed significantly better performance. ## related work early work proposed rule-based approaches to generate questions @xcite ). recent work using neural models for high-quality generation @xcite , @xcite , @xcite ). several works have aimed to control hallucination in different nlp tasks like summarization @xcite , @xcite , @xcite ), @xcite ), generative question answering @xcite , @xcite , @xcite ), data to text @xcite , @xcite , @xcite ), and machine translation @xcite , @xcite , @xcite ). input: as the president of south korea, she turned out to be heavily influenced by a cult and giving them access to political documents and basically letting that cult run the country through her. one of her "speech-writers" was a religious figure in this cult that totally exploited the president, who pretty much only got elected because her father had been president, using her for money and power and wealth. the people of south korea got upset when they learned that the real power that was running their country was the religious group that their president belonged to. normal finetuning: what did kim jong un do wrong? rare word delexicalization + multiple (ours): why did the people of south korea vote out the president of the south korea? input: as i understand it, there's no established *process* for removing the mayor from his post. if he doesn't resign, there's no mechanism for removing him. really, i don't understand how this guy hasn't lost his job yet. normal finetuning: why hasn't the mayor of chicago been fired yet? rare word delexicalization + multiple (ours): why hasn't the mayor been fired yet? previous work has explored entity-based delexicalization in settings like adapting parser for a new language @xcite , valid reasoning chains in multi-hop question answering @xcite , and eliminating diachronic biases in fake news detection @xcite . ## methodology the objective is to generate relevant and entitylevel factually consistent questions which generalise across domains. for this, we propose novel de-lexicalization strategies combined with a multigeneration strategy. de-lexicalization involves replacing named entities with a special token or rare words during training/inference and replacing the original word after generation. the model's vocabulary is expanded to account for the special tokens used in the de-lexicalization strategies. ## datasets we use the supervised eli5 dataset @xcite for training. to ensure that the data is of high quality, we remove all the samples where the answer is short (having less than 50 words), or the question does not have a question mark. we use three publicly available datasets for evaluation across different domains, viz. ms marco @xcite , natural questions @xcite and sciq @xcite . we also scraped r/asklegal foot_0 , and r/askeconomics foot_1 for testing on finance and legal domains. ## conclusion in this paper, we study the entity-level factual inconsistency in question generation. our proposed strategy, rare-word de-lexicalization with multigeneration, improve consistency without significantly affecting traditional metrics across data domains. extensive experimental results further reinforce our claim.