Jialiang Xu

Selected Publications

(For full publication list please refer to my Google Scholar page. "*" denotes equal contribution)

1. NAACL 2024Findings

   SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models

   Shicheng Liu, Jialiang Xu, Wesley Tjangnaka, Sina Semnani, Chen Jie Yu, and 1 more author

   2024

   Abs arXiv Bib PDF

   Many knowledge sources consist of both structured information such as relational databases as well as unstructured free text. Building a conversational interface to such data sources is challenging. This paper introduces SUQL, Structured and Unstructured Query Language, the first formal executable representation that naturally covers compositions of structured and unstructured data queries. Specifically, it augments SQL with several free-text primitives to form a precise, succinct, and expressive representation. This paper also presents a conversational search agent based on large language models, including a few-shot contextual semantic parser for SUQL. To validate our approach, we introduce a dataset consisting of crowdsourced questions and conversations about real restaurants. Over 51% of the questions in the dataset require both structured and unstructured data, suggesting that it is a common phenomenon. We show that our few-shot conversational agent based on SUQL finds an entity satisfying all user requirements 89.3% of the time, compared to just 65.0% for a strong and commonly used baseline.

   @misc{liu2024suql,
     title = {{SUQL}: Conversational Search over Structured and Unstructured Data with Large Language Models},
     author = {Liu, Shicheng and Xu, Jialiang and Tjangnaka, Wesley and Semnani, Sina and Yu, Chen Jie and Lam, Monica},
     year = {2024},
     url = {https://openreview.net/forum?id=DoSQeeVlUO},
     acc_type = {Findings}
   }

2. EMNLP 2022

   Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems

   Jialiang Xu, Mengyu Zhou, Xinyi He, Shi Han, and Dongmei Zhang

   2022

   Abs arXiv Bib PDF Poster Slides

   Numerical Question Answering is the task of answering questions that require numerical capabilities. Previous works introduce general adversarial attacks to Numerical Question Answering, while not systematically exploring numerical capabilities specific to the topic. In this paper, we propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. A series of numerical capabilities are highlighted, and corresponding dataset perturbations are designed. Empirical results indicate that existing systems are severely challenged by these perturbations. E.g., Graph2Tree experienced a 53.83% absolute accuracy drop against the “Extra” perturbation on ASDiv-a, and BART experienced 13.80% accuracy drop against the “Language” perturbation on the numerical subset of DROP. As a counteracting approach, we also investigate the effectiveness of applying perturbations as data augmentation to relieve systems’ lack of robust numerical capabilities. With experiment analysis and empirical studies, it is demonstrated that Numerical Question Answering with robust numerical capabilities is still to a large extent an open question. We discuss future directions of Numerical Question Answering and summarize guidelines on future dataset collection and system design.

     doi = {10.48550/ARXIV.2211.07455},
     author = {Xu, Jialiang and Zhou, Mengyu and He, Xinyi and Han, Shi and Zhang, Dongmei},
     keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
     title = {Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems},
     publisher = {arXiv},
     year = {2022},
     copyright = {arXiv.org perpetual, non-exclusive license},
   }

3. ArXiv

   LUNA: Language Understanding with Number Augmentations on Transformers via Number Plugins and Pre-training

   Hongwei Han*, Jialiang Xu*, Mengyu Zhou, Yijia Shao, Shi Han, and 1 more author

   2022

   Abs arXiv Bib PDF

   Transformers are widely used in NLP tasks. However, current approaches to leveraging transformers to understand language expose one weak spot: Number understanding. In some scenarios, numbers frequently occur, especially in semi-structured data like tables. But current approaches to rich-number tasks with transformer-based language models abandon or lose some of the numeracy information - e.g., breaking numbers into sub-word tokens - which leads to many number-related errors. In this paper, we propose the LUNA framework which improves the numerical reasoning and calculation capabilities of transformer-based language models. With the number plugin of NumTok and NumBed, LUNA represents each number as a whole to model input. With number pre-training, including regression loss and model distillation, LUNA bridges the gap between number and vocabulary embeddings. To the best of our knowledge, this is the first work that explicitly injects numeracy capability into language models using Number Plugins. Besides evaluating toy models on toy tasks, we evaluate LUNA on three large-scale transformer models (RoBERTa, BERT, TabBERT) over three different downstream tasks (TATQA, TabFact, CrediTrans), and observe the performances of language models are constantly improved by LUNA. The augmented models also improve the official baseline of TAT-QA (EM: 50.15 -> 59.58) and achieve SOTA performance on CrediTrans (F1 = 86.17).

     doi = {10.48550/ARXIV.2212.02691},
     author = {Han*, Hongwei and Xu*, Jialiang and Zhou, Mengyu and Shao, Yijia and Han, Shi and Zhang, Dongmei},
     keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
     title = {LUNA: Language Understanding with Number Augmentations on Transformers via Number Plugins and Pre-training},
     publisher = {arXiv},
     year = {2022},
     copyright = {arXiv.org perpetual, non-exclusive license},
   }