How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting MQR dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.Comment: AAAI 202

Answering Unanswered Questions through Semantic Reformulations in Spoken QA

Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencies, errors, and informal syntax or phrasing. This is a major challenge in QA, causing unanswered questions or irrelevant answers, and leading to bad user experiences. We analyze failed QA requests to identify core challenges: lexical gaps, proposition types, complex syntactic structure, and high specificity. We propose a Semantic Question Reformulation (SURF) model offering three linguistically-grounded operations (repair, syntactic reshaping, generalization) to rewrite questions to facilitate answering. Offline evaluation on 1M unanswered questions from a leading voice assistant shows that SURF significantly improves answer rates: up to 24% of previously unanswered questions obtain relevant answers (75%). Live deployment shows positive impact for millions of customers with unanswered questions; explicit relevance feedback shows high user satisfaction.Comment: Accepted by ACL 2023 Industry Trac

Human evaluation and statistical analyses on machine reading comprehension, question generation and open-domain dialogue

Evaluation is a critical element in the development process of many natural language based systems. In this thesis, we will present critical analyses of standard evaluation methodologies applied in the following Natural Language Processing (NLP) domains: machine reading comprehension (MRC), question generation (QG), and open-domain dialogue. Generally speaking, systems from tasks like MRC are usually evaluated by comparing the similarity between hand-crafted references and system generated outputs using automatic evaluation metrics, thus these metrics are mainly borrowed from other NLP tasks that have been well-developed, such as machine translation and text summarization. Meanwhile, the evaluation of QG and dialogues is even a known open problem as such tasks do not have the corresponding references for computing the similarity, and human evaluation is indispensable when assessing the performance of the systems from these tasks. However, human evaluation is unfortunately not always valid because: i) human evaluation may cost too much and be hard to deploy when experts are involved; ii) human assessors can lack reliability in the crowd-sourcing environment. To overcome the challenges from both automatic metrics and human evaluation, we first design specific crowdsourcing human evaluation methods for these three target tasks, respectively. We then show that these human evaluation methods are reproducible, highly reliable, easy to deploy, and cost-effective. Additionally, with the data collected from our experiments, we measure the accuracy of existing automatic metrics and analyse the potential limitations and disadvantages of the direct application of these metrics. Furthermore, in allusion to the specific features of different tasks, we provide detailed statistical analyses on the collected data to discover their underlying trends, and further give suggestions about the directions to improving systems on different aspects