Rui Zhang


2024

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Large Language Models for Mathematical Reasoning: Progresses and Challenges
Janice Ahn | Rishu Verma | Renze Lou | Di Liu | Rui Zhang | Wenpeng Yin
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the development of Large Language Models (LLMs) geared towards the automated resolution of mathematical problems. However, the landscape of mathematical problem types is vast and varied, with LLM-oriented techniques undergoing evaluation across diverse datasets and settings. This diversity makes it challenging to discern the true advancements and obstacles within this burgeoning field. This survey endeavors to address four pivotal dimensions: i) a comprehensive exploration of the various mathematical problems and their corresponding datasets that have been investigated; ii) an examination of the spectrum of LLM-oriented techniques that have been proposed for mathematical problem-solving; iii) an overview of factors and concerns affecting LLMs in solving math; and iv) an elucidation of the persisting challenges within this domain. To the best of our knowledge, this survey stands as one of the first extensive examinations of the landscape of LLMs in the realm of mathematics, providing a holistic perspective on the current state, accomplishments, and future challenges in this rapidly evolving field.

2023

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Intent Discovery with Frame-guided Semantic Regularization and Augmentation
Yajing Sun | Rui Zhang | Jingyuan Yang | Wei Peng
Findings of the Association for Computational Linguistics: ACL 2023

Most existing intent discovery methods leverage representation learning and clustering to transfer the prior knowledge of known intents to unknown ones. The learned representations are limited to the syntactic forms of sentences, therefore, fall short of recognizing adequate variations under the same meaning of unknown intents. This paper proposes an approach utilizing frame knowledge as conceptual semantic guidance to bridge the gap between known intents representation learning and unknown intents clustering. Specifically, we employ semantic regularization to minimize the bidirectional KL divergence between model predictions for frame-based and sentence-based samples. Moreover, we construct a frame-guided data augmenter to capture intent-friendly semantic information and implement contrastive clustering learning for unsupervised sentence embedding. Extensive experiments on two benchmark datasets show that our method achieves substantial improvements in accuracy (5%+) compared to solid baselines.

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Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses
Aysa Fan | Haoran Zhang | Luc Paquette | Rui Zhang
Findings of the Association for Computational Linguistics: EMNLP 2023

In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts for GPT4, guiding it to generate code-tracing questions based on code snippets and descriptions. We established a set of human evaluation metrics to assess the quality of questions produced by the model compared to those created by human experts. Our analysis provides insights into the capabilities and potential of LLMs in generating diverse code-tracing questions. Additionally, we present a unique dataset of human and LLM-generated tracing questions, serving as a valuable resource for both the education and NLP research communities. This work contributes to the ongoing dialogue on the potential uses of LLMs in educational settings.

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Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Sarkar Snigdha Sarathi Das | Haoran Zhang | Peng Shi | Wenpeng Yin | Rui Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Unified Sequence Labeling that articulates different sequence labeling problems such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized sequence-to-sequence format opens up the opportunity to make the maximum utilization of large language model knowledge toward structured prediction. Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format. This significantly bounds its usefulness in data-limited settings where finetuning large models cannot properly generalize to the target format. To address this challenge and leverage PLM knowledge effectively, we propose FISH-DIP, a sample-aware dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples, during the fine-tuning process. By leveraging the dynamism of sparsity, our approach mitigates the impact of well-learned samples and prioritizes underperforming instances for improvement in generalization. Across five tasks of sequence labeling, we demonstrate that FISH-DIP can smoothly optimize the model in low resource settings offering upto 40% performance improvements over full fine-tuning depending on target evaluation settings. Also, compared to in-context learning and other parameter-efficient fine-tuning approaches, FISH-DIP performs comparably or better, notably in extreme low-resource settings. The source code of FISH-DIP will be available at [this URL](https://github.com/psunlpgroup/FISH-DIP)

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FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
Nan Zhang | Yusen Zhang | Wu Guo | Prasenjit Mitra | Rui Zhang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Summaries of medical text shall be faithful by being consistent and factual with source inputs, which is an important but understudied topic for safety and efficiency in healthcare. In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks. Our investigation reveals that current summarization models often produce unfaithful outputs for medical input text. We then introduce FaMeSumm, a framework to improve faithfulness by fine-tuning pre-trained language models based on medical knowledge. FaMeSumm performs contrastive learning on designed sets of faithful and unfaithful summaries, and it incorporates medical terms and their contexts to encourage faithful generation of medical terms. We conduct comprehensive experiments on three datasets in two languages: health question and radiology report summarization datasets in English, and a patient-doctor dialogue dataset in Chinese. Results demonstrate that FaMeSumm is flexible and effective by delivering consistent improvements over mainstream language models such as BART, T5, mT5, and PEGASUS, yielding state-of-the-art performances on metrics for faithfulness and general quality. Human evaluation by doctors also shows that FaMeSumm generates more faithful outputs. Our code is available at https://github.com/psunlpgroup/FaMeSumm.

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ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining
Ranran Haoran Zhang | Aysa Xuemo Fan | Rui Zhang
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

A universal classification model aims to generalize to diverse classification tasks in both zero and few shot settings. A promising way toward universal classification is to cast heterogeneous data formats into a dataset-agnostic “meta-task” (e.g., textual entailment, question answering) then pretrain a model on the combined meta dataset. The existing work is either pretrained on specific subsets of classification tasks, or pretrained on both classification and generation data but the model could not fulfill its potential in universality and reliability. These also leave a massive amount of annotated data under-exploited. To fill these gaps, we propose ConEntail, a new framework for universal zero and few shot classification with supervised contrastive pretraining. Our unified meta-task for classification is based on nested entailment. It can be interpreted as “Does sentence a entails [sentence b entails label c]”. This formulation enables us to make better use of 57 annotated classification datasets for supervised contrastive pretraining and universal evaluation. In this way, ConEntail helps the model (1) absorb knowledge from different datasets, and (2) gain consistent performance gain with more pretraining data. In experiments, we compare our model with discriminative and generative models pretrained on the same dataset. The results confirm that our framework effectively exploits existing annotated data and consistently outperforms baselines in both zero (9.4% average improvement) and few shot settings (3.5% average improvement). Our code is available in supplementary materials.

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HIT-MI&T Lab’s Submission to Eval4NLP 2023 Shared Task
Rui Zhang | Fuhai Song | Hui Huang | Jinghao Yuan | Muyun Yang | Tiejun Zhao
Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems

Recently, Large Language Models (LLMs) have boosted the research in natural language processing and shown impressive capabilities across numerous domains, including machine translation evaluation. This paper presents our methods developed for the machine translation evaluation sub-task of the Eval4NLP 2023 Shared Task. Based on the provided LLMs, we propose a generation-based method as well as a probability-based method to perform evaluation, explore different strategies when selecting the demonstrations for in-context learning, and try different ensemble methods to further improve the evaluation accuracy. The experiment results on the development set and test set demonstrate the effectiveness of our proposed method.

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MACSum: Controllable Summarization with Mixed Attributes
Yusen Zhang | Yang Liu | Ziyi Yang | Yuwei Fang | Yulong Chen | Dragomir Radev | Chenguang Zhu | Michael Zeng | Rui Zhang
Transactions of the Association for Computational Linguistics, Volume 11

Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing work has to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on most metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.

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XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
Yusen Zhang | Jun Wang | Zhiguo Wang | Rui Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Cross-Lingual Semantic Parsing (CLSP) aims to translate queries in multiple natural languages (NLs) into meaning representations (MRs) such as SQL, lambda calculus, and logic forms. However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains. We use XSemPLR to conduct a comprehensive benchmark study on a wide range of multilingual language models including encoder-based models (mBERT, XLM-R), encoder-decoder models (mBART, mT5), and decoder-based models (Codex, BLOOM). We design 6 experiment settings covering various lingual combinations (monolingual, multilingual, cross-lingual) and numbers of learning samples (full dataset, few-shot, and zero-shot). Our experiments show that encoder-decoder models (mT5) achieve the highest performance compared with other popular models, and multilingual training can further improve the average performance. Notably, multilingual large language models (e.g., BLOOM) are still inadequate to perform CLSP tasks. We also find that the performance gap between monolingual training and cross-lingual transfer learning is still significant for multilingual models, though it can be mitigated by cross-lingual few-shot training. Our dataset and code are available at https://github.com/psunlpgroup/XSemPLR.

2022

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FeTaQA: Free-form Table Question Answering
Linyong Nan | Chiachun Hsieh | Ziming Mao | Xi Victoria Lin | Neha Verma | Rui Zhang | Wojciech Kryściński | Hailey Schoelkopf | Riley Kong | Xiangru Tang | Mutethia Mutuma | Ben Rosand | Isabel Trindade | Renusree Bandaru | Jacob Cunningham | Caiming Xiong | Dragomir Radev | Dragomir Radev
Transactions of the Association for Computational Linguistics, Volume 10

Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.

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XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing
Peng Shi | Rui Zhang | He Bai | Jimmy Lin
Findings of the Association for Computational Linguistics: EMNLP 2022

In-context learning using large language models has recently shown surprising results for semantic parsing tasks such as Text-to-SQL translation. Prompting GPT-3 or Codex using several examples of question-SQL pairs can produce excellent results, comparable to state-of-the-art finetuning-based models. However, existing work primarily focuses on English datasets, and it is unknown whether large language models can serve as competitive semantic parsers for other languages. To bridge this gap, our work focuses on cross-lingual Text-to-SQL semantic parsing for translating non-English utterances into SQL queries based on an English schema. We consider a zero-shot transfer learning setting with the assumption that we do not have any labeled examples in the target language (but have annotated examples in English).This work introduces the XRICL framework, which learns to retrieve relevant English exemplars for a given query to construct prompts. We also include global translation exemplars for a target language to facilitate the translation process for large language models. To systematically evaluate our model, we construct two new benchmark datasets, XSpider and XKaggle-dbqa, which include questions in Chinese, Vietnamese, Farsi, and Hindi.Our experiments show that XRICL effectively leverages large pre-trained language models to outperform existing baselines. Data and code are publicly available at https://github.com/Impavidity/XRICL.

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Proceedings of the Workshop on Multilingual Information Access (MIA)
Akari Asai | Eunsol Choi | Jonathan H. Clark | Junjie Hu | Chia-Hsuan Lee | Jungo Kasai | Shayne Longpre | Ikuya Yamada | Rui Zhang
Proceedings of the Workshop on Multilingual Information Access (MIA)

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MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
Akari Asai | Shayne Longpre | Jungo Kasai | Chia-Hsuan Lee | Rui Zhang | Junjie Hu | Ikuya Yamada | Jonathan H. Clark | Eunsol Choi
Proceedings of the Workshop on Multilingual Information Access (MIA)

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages. In this task, we adapted two large-scale cross-lingual open-retrieval QA datasets in 14 typologically diverse languages, and newly annotated open-retrieval QA data in 2 underrepresented languages: Tagalog and Tamil. Four teams submitted their systems. The best constrained system uses entity-aware contextualized representations for document retrieval, thereby achieving an average F1 score of 31.6, which is 4.1 F1 absolute higher than the challenging baseline. The best system obtains particularly significant improvements in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores. The best unconstrained system achieves 32.2 F1, outperforming our baseline by 4.5 points.

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Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)
Wenhu Chen | Xinyun Chen | Zhiyu Chen | Ziyu Yao | Michihiro Yasunaga | Tao Yu | Rui Zhang
Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

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SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
Yusen Zhang | Ansong Ni | Ziming Mao | Chen Henry Wu | Chenguang Zhu | Budhaditya Deb | Ahmed Awadallah | Dragomir Radev | Rui Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unable to efficiently process long text for many summarization tasks. In this paper, we propose SummN, a simple, flexible, and effective multi-stage framework for input texts that are longer than the maximum context length of typical pretrained LMs. SummN first splits the data samples and generates a coarse summary in multiple stages and then produces the final fine-grained summary based on it. Our framework can process input text of arbitrary length by adjusting the number of stages while keeping the LM input size fixed. Moreover, it can deal with both single-source documents and dialogues, and it can be used on top of different backbone abstractive summarization models. To the best of our knowledge, SummN is the first multi-stage split-then-summarize framework for long input summarization. Our experiments demonstrate that SummN outperforms previous state-of-the-art methods by improving ROUGE scores on three long meeting summarization datasets AMI, ICSI, and QMSum, two long TV series datasets from SummScreen, and a long document summarization dataset GovReport. Our data and code are available at https://github.com/psunlpgroup/Summ-N.

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DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Ziming Mao | Chen Henry Wu | Ansong Ni | Yusen Zhang | Rui Zhang | Tao Yu | Budhaditya Deb | Chenguang Zhu | Ahmed Awadallah | Dragomir Radev
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Transformer-based models have achieved state-of-the-art performance on short-input summarization. However, they still struggle with summarizing longer text. In this paper, we present DYLE, a novel dynamic latent extraction approach for abstractive long-input summarization. DYLE jointly trains an extractor and a generator and treats the extracted text snippets as the latent variable, allowing dynamic snippet-level attention weights during decoding. To provide adequate supervision, we propose simple yet effective heuristics for oracle extraction as well as a consistency loss term, which encourages the extractor to approximate the averaged dynamic weights predicted by the generator. We evaluate our method on different long-document and long-dialogue summarization tasks: GovReport, QMSum, and arXiv. Experiment results show that DYLE outperforms all existing methods on GovReport and QMSum, with gains up to 6.1 ROUGE, while yielding strong results on arXiv. Further analysis shows that the proposed dynamic weights provide interpretability of our generation process.

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An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation
Shiquan Yang | Rui Zhang | Sarah Erfani | Jey Han Lau
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We study the interpretability issue of task-oriented dialogue systems in this paper. Previously, most neural-based task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains. Since deriving reasoning chains requires multi-hop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design. To overcome this, we propose a two-phase approach that consists of a hypothesis generator and a reasoner. We first obtain multiple hypotheses, i.e., potential operations to perform the desired task, through the hypothesis generator. Each hypothesis is then verified by the reasoner, and the valid one is selected to conduct the final prediction. The whole system is trained by exploiting raw textual dialogues without using any reasoning chain annotations. Experimental studies on two public benchmark datasets demonstrate that the proposed approach not only achieves better results, but also introduces an interpretable decision process.

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CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
Sarkar Snigdha Sarathi Das | Arzoo Katiyar | Rebecca Passonneau | Rui Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains. Existing approaches only learn class-specific semantic features and intermediate representations from source domains. This affects generalizability to unseen target domains, resulting in suboptimal performances. To this end, we present CONTaiNER, a novel contrastive learning technique that optimizes the inter-token distribution distance for Few-Shot NER. Instead of optimizing class-specific attributes, CONTaiNER optimizes a generalized objective of differentiating between token categories based on their Gaussian-distributed embeddings. This effectively alleviates overfitting issues originating from training domains. Our experiments in several traditional test domains (OntoNotes, CoNLL’03, WNUT ‘17, GUM) and a new large scale Few-Shot NER dataset (Few-NERD) demonstrate that on average, CONTaiNER outperforms previous methods by 3%-13% absolute F1 points while showing consistent performance trends, even in challenging scenarios where previous approaches could not achieve appreciable performance.

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MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
Yilun Zhao | Yunxiang Li | Chenying Li | Rui Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community. However, existing question answering (QA) benchmarks over hybrid data only include a single flat table in each document and thus lack examples of multi-step numerical reasoning across multiple hierarchical tables. To facilitate data analytical progress, we construct a new large-scale benchmark, MultiHiertt, with QA pairs over Multi Hierarchical Tabular and Textual data. MultiHiertt is built from a wealth of financial reports and has the following unique characteristics: 1) each document contain multiple tables and longer unstructured texts; 2) most of tables contained are hierarchical; 3) the reasoning process required for each question is more complex and challenging than existing benchmarks; and 4) fine-grained annotations of reasoning processes and supporting facts are provided to reveal complex numerical reasoning. We further introduce a novel QA model termed MT2Net, which first applies facts retrieving to extract relevant supporting facts from both tables and text and then uses a reasoning module to perform symbolic reasoning over retrieved facts. We conduct comprehensive experiments on various baselines. The experimental results show that MultiHiertt presents a strong challenge for existing baselines whose results lag far behind the performance of human experts. The dataset and code are publicly available at https://github.com/psunlpgroup/MultiHiertt.

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Contrastive Data and Learning for Natural Language Processing
Rui Zhang | Yangfeng Ji | Yue Zhang | Rebecca J. Passonneau
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts

Current NLP models heavily rely on effective representation learning algorithms. Contrastive learning is one such technique to learn an embedding space such that similar data sample pairs have close representations while dissimilar samples stay far apart from each other. It can be used in supervised or unsupervised settings using different loss functions to produce task-specific or general-purpose representations. While it has originally enabled the success for vision tasks, recent years have seen a growing number of publications in contrastive NLP. This first line of works not only delivers promising performance improvements in various NLP tasks, but also provides desired characteristics such as task-agnostic sentence representation, faithful text generation, data-efficient learning in zero-shot and few-shot settings, interpretability and explainability. In this tutorial, we aim to provide a gentle introduction to the fundamentals of contrastive learning approaches and the theory behind them. We then survey the benefits and the best practices of contrastive learning for various downstream NLP applications including Text Classification, Question Answering, Summarization, Text Generation, Interpretability and Explainability, Commonsense Knowledge and Reasoning, Vision-and-Language.This tutorial intends to help researchers in the NLP and computational linguistics community to understand this emerging topic and promote future research directions of using contrastive learning for NLP applications.

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UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Tianbao Xie | Chen Henry Wu | Peng Shi | Ruiqi Zhong | Torsten Scholak | Michihiro Yasunaga | Chien-Sheng Wu | Ming Zhong | Pengcheng Yin | Sida I. Wang | Victor Zhong | Bailin Wang | Chengzu Li | Connor Boyle | Ansong Ni | Ziyu Yao | Dragomir Radev | Caiming Xiong | Lingpeng Kong | Rui Zhang | Noah A. Smith | Luke Zettlemoyer | Tao Yu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg.

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ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
Yilun Zhao | Linyong Nan | Zhenting Qi | Rui Zhang | Dragomir Radev
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction. Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates. We model the table pre-training task as a sequence generation task and pre-train ReasTAP to generate precise answers of the synthetic examples. ReasTAP is evaluated on four benchmarks covering three downstream tasks including 1) WikiSQL-Weak and WikiTQ for Table Question Answering, 2) TabFact for Table Fact Verification, and 3) LogicNLG for Faithful Table-to-Text Generation. Experimental results demonstrate that ReasTAP achieves new state-of-the-art results on all of them and delivers a significant improvement under low-resource setting. Our code is publicly available at https://github.com/Yale-LILY/ReasTAP.

2021

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Cross-Lingual Training of Dense Retrievers for Document Retrieval
Peng Shi | Rui Zhang | He Bai | Jimmy Lin
Proceedings of the 1st Workshop on Multilingual Representation Learning

Dense retrieval has shown great success for passage ranking in English. However, its effectiveness for non-English languages remains unexplored due to limitation in training resources. In this work, we explore different transfer techniques for document ranking from English annotations to non-English languages. Our experiments reveal that zero-shot model-based transfer using mBERT improves search quality. We find that weakly-supervised target language transfer is competitive compared to generation-based target language transfer, which requires translation models.

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Latent Reasoning for Low-Resource Question Generation
Xinting Huang | Jianzhong Qi | Yu Sun | Rui Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Logic-Consistency Text Generation from Semantic Parses
Chang Shu | Yusen Zhang | Xiangyu Dong | Peng Shi | Tao Yu | Rui Zhang
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Grouped-Attention for Content-Selection and Content-Plan Generation
Bayu Distiawan Trisedya | Xiaojie Wang | Jianzhong Qi | Rui Zhang | Qingjun Cui
Findings of the Association for Computational Linguistics: EMNLP 2021

Content-planning is an essential part of data-to-text generation to determine the order of data mentioned in generated texts. Recent neural data-to-text generation models employ Pointer Networks to explicitly learn content-plan given a set of attributes as input. They use LSTM to encode the input, which assumes a sequential relationship in the input. This may be sub-optimal to encode a set of attributes, where the attributes have a composite structure: the attributes are disordered while each attribute value is an ordered list of tokens. We handle this problem by proposing a neural content-planner that can capture both local and global contexts of such a structure. Specifically, we propose a novel attention mechanism called GSC-attention. A key component of the GSC-attention is grouped-attention, which is token-level attention constrained within each input attribute that enables our proposed model captures both local and global context. Moreover, our content-planner explicitly learns content-selection, which is integrated into the content-planner to select the most important data to be included in the generated text via an attention masking procedure. Experimental results show that our model outperforms the competitors by 4.92%, 4.70%, and 16.56% in terms of Damerau-Levenshtein Distance scores on three real-world datasets.

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TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Adaku Uchendu | Zeyu Ma | Thai Le | Rui Zhang | Dongwon Lee
Findings of the Association for Computational Linguistics: EMNLP 2021

Recent progress in generative language models has enabled machines to generate astonishingly realistic texts. While there are many legitimate applications of such models, there is also a rising need to distinguish machine-generated texts from human-written ones (e.g., fake news detection). However, to our best knowledge, there is currently no benchmark environment with datasets and tasks to systematically study the so-called ”Turing Test” problem for neural text generation methods. In this work, we present the TURINGBENCH benchmark environment, which is comprised of (1) a dataset with 200K human- or machine-generated samples across 20 labels Human, GPT-1, GPT-2_small, GPT-2_medium, GPT-2_large,GPT-2_xl, GPT-2_PyTorch, GPT-3, GROVER_base, GROVER_large, GROVER_mega, CTRL, XLM, XLNET_base, XLNET_large, FAIR_wmt19, FAIR_wmt20, TRANSFORMER_XL, PPLM_distil, PPLM_gpt2, (2) two benchmark tasks–i.e., Turing Test (TT) and Authorship Attribution (AA), and (3) a website with leaderboards. Our preliminary experimental results using TURINGBENCH show that GPT-3 and FAIR_wmt20 are the current winners, among all language models tested, in generating the most human-like indistinguishable texts with the lowest F1 score by five state-of-the-art TT detection models. The TURINGBENCH is available at: https://turingbench.ist.psu.edu/

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An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next
Yusen Zhang | Ansong Ni | Tao Yu | Rui Zhang | Chenguang Zhu | Budhaditya Deb | Asli Celikyilmaz | Ahmed Hassan Awadallah | Dragomir Radev
Findings of the Association for Computational Linguistics: EMNLP 2021

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challenge to current summarization models, as the dialogue length typically exceeds the input limits imposed by recent transformer-based pre-trained models, and the interactive nature of dialogues makes relevant information more context-dependent and sparsely distributed than news articles. In this work, we perform a comprehensive study on long dialogue summarization by investigating three strategies to deal with the lengthy input problem and locate relevant information: (1) extended transformer models such as Longformer, (2) retrieve-then-summarize pipeline models with several dialogue utterance retrieval methods, and (3) hierarchical dialogue encoding models such as HMNet. Our experimental results on three long dialogue datasets (QMSum, MediaSum, SummScreen) show that the retrieve-then-summarize pipeline models yield the best performance. We also demonstrate that the summary quality can be further improved with a stronger retrieval model and pretraining on proper external summarization datasets.

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DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan | Dragomir Radev | Rui Zhang | Amrit Rau | Abhinand Sivaprasad | Chiachun Hsieh | Xiangru Tang | Aadit Vyas | Neha Verma | Pranav Krishna | Yangxiaokang Liu | Nadia Irwanto | Jessica Pan | Faiaz Rahman | Ahmad Zaidi | Mutethia Mutuma | Yasin Tarabar | Ankit Gupta | Tao Yu | Yi Chern Tan | Xi Victoria Lin | Caiming Xiong | Richard Socher | Nazneen Fatema Rajani
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.

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Cross-language Sentence Selection via Data Augmentation and Rationale Training
Yanda Chen | Chris Kedzie | Suraj Nair | Petra Galuscakova | Rui Zhang | Douglas Oard | Kathleen McKeown
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data. Moreover, when a rationale training secondary objective is applied to encourage the model to match word alignment hints from a phrase-based statistical machine translation model, consistent improvements are seen across three language pairs (English-Somali, English-Swahili and English-Tagalog) over a variety of state-of-the-art baselines.

2020

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MATERIALizing Cross-Language Information Retrieval: A Snapshot
Petra Galuscakova | Douglas Oard | Joe Barrow | Suraj Nair | Shing Han-Chin | Elena Zotkina | Ramy Eskander | Rui Zhang
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)

At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems’ abilities to find Lithuanian documents based on English queries. Subsequently, both the Lithuanian test collection and results from all three teams were made available for detailed analysis. This paper capitalizes on that opportunity to begin to look at what’s working well at this stage of the program, and to identify some promising directions for future work.

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GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems
Shiquan Yang | Rui Zhang | Sarah Erfani
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs. There are two challenges for such systems: one is how to effectively incorporate external knowledge bases (KBs) into the learning framework; the other is how to accurately capture the semantics of dialogue history. In this paper, we address these two challenges by exploiting the graph structural information in the knowledge base and in the dependency parsing tree of the dialogue. To effectively leverage the structural information in dialogue history, we propose a new recurrent cell architecture which allows representation learning on graphs. To exploit the relations between entities in KBs, the model combines multi-hop reasoning ability based on the graph structure. Experimental results show that the proposed model achieves consistent improvement over state-of-the-art models on two different task-oriented dialogue datasets.

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Generalizable and Explainable Dialogue Generation via Explicit Action Learning
Xinting Huang | Jianzhong Qi | Yu Sun | Rui Zhang
Findings of the Association for Computational Linguistics: EMNLP 2020

Response generation for task-oriented dialogues implicitly optimizes two objectives at the same time: task completion and language quality. Conditioned response generation serves as an effective approach to separately and better optimize these two objectives. Such an approach relies on system action annotations which are expensive to obtain. To alleviate the need of action annotations, latent action learning is introduced to map each utterance to a latent representation. However, this approach is prone to over-dependence on the training data, and the generalization capability is thus restricted. To address this issue, we propose to learn natural language actions that represent utterances as a span of words. This explicit action representation promotes generalization via the compositional structure of language. It also enables an explainable generation process. Our proposed unsupervised approach learns a memory component to summarize system utterances into a short span of words. To further promote a compact action representation, we propose an auxiliary task that restores state annotations as the summarized dialogue context using the memory component. Our proposed approach outperforms latent action baselines on MultiWOZ, a benchmark multi-domain dataset.

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Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation
Xinting Huang | Jianzhong Qi | Yu Sun | Rui Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Dialogue policy optimization often obtains feedback until task completion in task-oriented dialogue systems. This is insufficient for training intermediate dialogue turns since supervision signals (or rewards) are only provided at the end of dialogues. To address this issue, reward learning has been introduced to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards. This approach requires complete state-action annotations of human-to-human dialogues (i.e., expert demonstrations), which is labor intensive. To overcome this limitation, we propose a novel reward learning approach for semi-supervised policy learning. The proposed approach learns a dynamics model as the reward function which models dialogue progress (i.e., state-action sequences) based on expert demonstrations, either with or without annotations. The dynamics model computes rewards by predicting whether the dialogue progress is consistent with expert demonstrations. We further propose to learn action embeddings for a better generalization of the reward function. The proposed approach outperforms competitive policy learning baselines on MultiWOZ, a benchmark multi-domain dataset.

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ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Nazneen Fatema Rajani | Rui Zhang | Yi Chern Tan | Stephan Zheng | Jeremy Weiss | Aadit Vyas | Abhijit Gupta | Caiming Xiong | Richard Socher | Dragomir Radev
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at https://github.com/salesforce/esprit.

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Proceedings of the First Workshop on Interactive and Executable Semantic Parsing
Ben Bogin | Srinivasan Iyer | Xi Victoria Lin | Dragomir Radev | Alane Suhr | Panupong | Caiming Xiong | Pengcheng Yin | Tao Yu | Rui Zhang | Victor Zhong
Proceedings of the First Workshop on Interactive and Executable Semantic Parsing

2019

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Neural Relation Extraction for Knowledge Base Enrichment
Bayu Distiawan Trisedya | Gerhard Weikum | Jianzhong Qi | Rui Zhang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end-to-end manner. Previous studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into the KB space. This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model. We collect high-quality training data by distant supervision with co-reference resolution and paraphrase detection. We propose an n-gram based attention model that captures multi-word entity names in a sentence. Our model employs jointly learned word and entity embeddings to support named entity disambiguation. Finally, our model uses a modified beam search and a triple classifier to help generate high-quality triples. Our model outperforms state-of-the-art baselines by 15.51% and 8.38% in terms of F1 score on two real-world datasets.

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This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Rui Zhang | Joel Tetreault
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email’s content. In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body. We create the first dataset for this task and find that email subject line generation favor extremely abstractive summary which differentiates it from news headline generation or news single document summarization. We then develop a novel deep learning method and compare it to several baselines as well as recent state-of-the-art text summarization systems. We also investigate the efficacy of several automatic metrics based on correlations with human judgments and propose a new automatic evaluation metric. Our system outperforms competitive baselines given both automatic and human evaluations. To our knowledge, this is the first work to tackle the problem of effective email subject line generation.

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Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
Rui Zhang | Caitlin Westerfield | Sungrok Shim | Garrett Bingham | Alexander Fabbri | William Hu | Neha Verma | Dragomir Radev
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in both source and target languages with four components, each of which is implemented as a term interaction-based deep neural network with cross-lingual word embeddings as input. By including query likelihood scores as extra features, our model effectively learns to rerank the retrieved documents by using a small number of relevance labels for low-resource language pairs. Due to the shared cross-lingual word embedding space, the model can also be directly applied to another language pair without any training label. Experimental results on the Material dataset show that our model outperforms the competitive translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual information retrieval tasks.

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SParC: Cross-Domain Semantic Parsing in Context
Tao Yu | Rui Zhang | Michihiro Yasunaga | Yi Chern Tan | Xi Victoria Lin | Suyi Li | Heyang Er | Irene Li | Bo Pang | Tao Chen | Emily Ji | Shreya Dixit | David Proctor | Sungrok Shim | Jonathan Kraft | Vincent Zhang | Caiming Xiong | Richard Socher | Dragomir Radev
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.

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Applications of Natural Language Processing in Clinical Research and Practice
Yanshan Wang | Ahmad Tafti | Sunghwan Sohn | Rui Zhang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials

Rapid growth in adoption of electronic health records (EHRs) has led to an unprecedented expansion in the availability of large longitudinal datasets. Large initiatives such as the Electronic Medical Records and Genomics (eMERGE) Network, the Patient-Centered Outcomes Research Network (PCORNet), and the Observational Health Data Science and Informatics (OHDSI) consortium, have been established and have reported successful applications of secondary use of EHRs in clinical research and practice. In these applications, natural language processing (NLP) technologies have played a crucial role as much of detailed patient information in EHRs is embedded in narrative clinical documents. Meanwhile, a number of clinical NLP systems, such as MedLEE, MetaMap/MetaMap Lite, cTAKES, and MedTagger have been developed and utilized to extract useful information from diverse types of clinical text, such as clinical notes, radiology reports, and pathology reports. Success stories in applying these tools have been reported widely. Despite the demonstrated success of NLP in the clinical domain, methodologies and tools developed for the clinical NLP are still underknown and underutilized by students and experts in the general NLP domain, mainly due to the limited exposure to EHR data. Through this tutorial, we would like to introduce NLP methodologies and tools developed in the clinical domain, and showcase the real-world NLP applications in clinical research and practice at Mayo Clinic (the No. 1 national hospital ranked by the U.S. News & World Report) and the University of Minnesota (the No. 41 best global universities ranked by the U.S. News & World Report). We will review NLP techniques in solving clinical problems and facilitating clinical research, the state-of-the art clinical NLP tools, and share collaboration experience with clinicians, as well as publicly available EHR data and medical resources, and finally conclude the tutorial with vast opportunities and challenges of clinical NLP. The tutorial will provide an overview of clinical backgrounds, and does not presume knowledge in medicine or health care. The goal of this tutorial is to encourage NLP researchers in the general domain (as opposed to the specialized clinical domain) to contribute to this burgeoning area. In this tutorial, we will first present an overview of clinical NLP. We will then dive into two subareas of clinical NLP in clinical research, including big data infrastructure for large-scale clinical NLP and advances of NLP in clinical research, and two subareas in clinical practice, including clinical information extraction and patient cohort retrieval using EHRs. Around 70% of the tutorial will review clinical problems, cutting-edge methodologies, and real-world clinical NLP tools while another 30% introduce use cases at Mayo Clinic and the University of Minnesota. Finally, we will conclude the tutorial with challenges and opportunities in this rapidly developing domain.

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CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Tao Yu | Rui Zhang | Heyang Er | Suyi Li | Eric Xue | Bo Pang | Xi Victoria Lin | Yi Chern Tan | Tianze Shi | Zihan Li | Youxuan Jiang | Michihiro Yasunaga | Sungrok Shim | Tao Chen | Alexander Fabbri | Zifan Li | Luyao Chen | Yuwen Zhang | Shreya Dixit | Vincent Zhang | Caiming Xiong | Richard Socher | Walter Lasecki | Dragomir Radev
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained from a Wizard-of-Oz (WOZ) collection of 3k dialogues querying 200 complex DBs spanning 138 domains. Each dialogue simulates a real-world DB query scenario with a crowd worker as a user exploring the DB and a SQL expert retrieving answers with SQL, clarifying ambiguous questions, or otherwise informing of unanswerable questions. When user questions are answerable by SQL, the expert describes the SQL and execution results to the user, hence maintaining a natural interaction flow. CoSQL introduces new challenges compared to existing task-oriented dialogue datasets: (1) the dialogue states are grounded in SQL, a domain-independent executable representation, instead of domain-specific slot value pairs, and (2) because testing is done on unseen databases, success requires generalizing to new domains. CoSQL includes three tasks: SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction. We evaluate a set of strong baselines for each task and show that CoSQL presents significant challenges for future research. The dataset, baselines, and leaderboard will be released at https://yale-lily.github.io/cosql.

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Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
Rui Zhang | Tao Yu | Heyang Er | Sungrok Shim | Eric Xue | Xi Victoria Lin | Tianze Shi | Caiming Xiong | Richard Socher | Dragomir Radev
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.

2018

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PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages
Yiqing Zhang | Jianzhong Qi | Rui Zhang | Chuandong Yin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Publication information in a researcher’s academic homepage provides insights about the researcher’s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8% in F1-score.

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SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
Tao Yu | Michihiro Yasunaga | Kai Yang | Rui Zhang | Dongxu Wang | Zifan Li | Dragomir Radev
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on a new large-scale text-to-SQL corpus containing databases with multiple tables and complex SQL queries containing multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 9.5% in exact matching accuracy. To our knowledge, we are the first to study this complex text-to-SQL task. Our task and models with the latest updates are available at https://yale-lily.github.io/seq2sql/spider.

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Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Tao Yu | Rui Zhang | Kai Yang | Michihiro Yasunaga | Dongxu Wang | Zifan Li | James Ma | Irene Li | Qingning Yao | Shanelle Roman | Zilin Zhang | Dragomir Radev
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present Spider, a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables covering 138 different domains. We define a new complex and cross-domain semantic parsing and text-to-SQL task so that different complicated SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Therefore, Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and have the exact same program in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 9.7% exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task with the most recent updates are publicly available at https://yale-lily.github.io/seq2sql/spider.

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TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation
Tao Yu | Zifan Li | Zilin Zhang | Rui Zhang | Dragomir Radev
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Interacting with relational databases through natural language helps users with any background easily query and analyze a vast amount of data. This requires a system that understands users’ questions and converts them to SQL queries automatically. In this paper, we present a novel approach TypeSQL which formats the problem as a slot filling task in a more reasonable way. In addition, TypeSQL utilizes type information to better understand rare entities and numbers in the questions. We experiment this idea on the WikiSQL dataset and outperform the prior art by 6% in much shorter time. We also show that accessing the content of databases can significantly improve the performance when users’ queries are not well-formed. TypeSQL can reach 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.

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Improving Text-to-SQL Evaluation Methodology
Catherine Finegan-Dollak | Jonathan K. Kummerfeld | Li Zhang | Karthik Ramanathan | Sesh Sadasivam | Rui Zhang | Dragomir Radev
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.

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GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
Bayu Distiawan Trisedya | Jianzhong Qi | Rui Zhang | Wei Wang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6%, 6.0%, and 16.4% in three common metrics BLEU, METEOR, and TER, respectively.

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Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering
Rui Zhang | Cícero Nogueira dos Santos | Michihiro Yasunaga | Bing Xiang | Dragomir Radev
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Coreference resolution aims to identify in a text all mentions that refer to the same real world entity. The state-of-the-art end-to-end neural coreference model considers all text spans in a document as potential mentions and learns to link an antecedent for each possible mention. In this paper, we propose to improve the end-to-end coreference resolution system by (1) using a biaffine attention model to get antecedent scores for each possible mention, and (2) jointly optimizing the mention detection accuracy and mention clustering accuracy given the mention cluster labels. Our model achieves the state-of-the-art performance on the CoNLL-2012 shared task English test set.

2017

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Graph-based Neural Multi-Document Summarization
Michihiro Yasunaga | Rui Zhang | Kshitijh Meelu | Ayush Pareek | Krishnan Srinivasan | Dragomir Radev
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

We propose a neural multi-document summarization system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node features. Through multiple layer-wise propagation, the GCN generates high-level hidden sentence features for salience estimation. We then use a greedy heuristic to extract salient sentences that avoid redundancy. In our experiments on DUC 2004, we consider three types of sentence relation graphs and demonstrate the advantage of combining sentence relations in graphs with the representation power of deep neural networks. Our model improves upon other traditional graph-based extractive approaches and the vanilla GRU sequence model with no graph, and it achieves competitive results against other state-of-the-art multi-document summarization systems.

2016

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N-ary Biographical Relation Extraction using Shortest Path Dependencies
Gitansh Khirbat | Jianzhong Qi | Rui Zhang
Proceedings of the Australasian Language Technology Association Workshop 2016

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Disambiguating Entities Referred by Web Endpoints using Tree Ensembles
Gitansh Khirbat | Jianzhong Qi | Rui Zhang
Proceedings of the Australasian Language Technology Association Workshop 2016

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Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Rui Zhang | Honglak Lee | Dragomir R. Radev
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Effects of Creativity and Cluster Tightness on Short Text Clustering Performance
Catherine Finegan-Dollak | Reed Coke | Rui Zhang | Xiangyi Ye | Dragomir Radev
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2014

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A Probabilistic Model for Learning Multi-Prototype Word Embeddings
Fei Tian | Hanjun Dai | Jiang Bian | Bin Gao | Rui Zhang | Enhong Chen | Tie-Yan Liu
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2011

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A Breadth-First Representation for Tree Matching in Large Scale Forest-Based Translation
Sumukh Ghodke | Steven Bird | Rui Zhang
Proceedings of 5th International Joint Conference on Natural Language Processing

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