Li Zhang


2021

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Visual Goal-Step Inference using wikiHow
Yue Yang | Artemis Panagopoulou | Qing Lyu | Li Zhang | Mark Yatskar | Chris Callison-Burch
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities. Past work in NLP has examined the task of goal-step inference for text. We introduce the visual analogue. We propose the Visual Goal-Step Inference (VGSI) task, where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. With a new dataset harvested from wikiHow consisting of 772,277 images representing human actions, we show that our task is challenging for state-of-the-art multimodal models. Moreover, the multimodal representation learned from our data can be effectively transferred to other datasets like HowTo100m, increasing the VGSI accuracy by 15 - 20%. Our task will facilitate multimodal reasoning about procedural events.

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Complementary Evidence Identification in Open-Domain Question Answering
Xiangyang Mou | Mo Yu | Shiyu Chang | Yufei Feng | Li Zhang | Hui Su
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages that covers full evidence from multiple aspects as to answer a complex question. To this end, we proposes a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. Our experiments demonstrate that our method considers the dependence within the supporting evidence and significantly improves the accuracy of complementary evidence selection in QA domain.

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Goal-Oriented Script Construction
Qing Lyu | Li Zhang | Chris Callison-Burch
Proceedings of the 14th International Conference on Natural Language Generation

The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.

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Multi-Level Gazetteer-Free Geocoding
Sayali Kulkarni | Shailee Jain | Mohammad Javad Hosseini | Jason Baldridge | Eugene Ie | Li Zhang
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates. The Earth’s surface is represented using space-filling curves that decompose the sphere into a hierarchical grid. MLG balances classification granularity and accuracy by combining losses across multiple levels and jointly predicting cells at different levels simultaneously. It obtains large gains without any gazetteer metadata, demonstrating that it can effectively learn the connection between text spans and coordinates—and thus makes it a gazetteer-free geocoder. Furthermore, MLG obtains state-of-the-art results for toponym resolution on three English datasets without any dataset-specific tuning.

2020

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Intent Detection with WikiHow
Li Zhang | Qing Lyu | Chris Callison-Burch
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Modern task-oriented dialog systems need to reliably understand users’ intents. Intent detection is even more challenging when moving to new domains or new languages, since there is little annotated data. To address this challenge, we present a suite of pretrained intent detection models which can predict a broad range of intended goals from many actions because they are trained on wikiHow, a comprehensive instructional website. Our models achieve state-of-the-art results on the Snips dataset, the Schema-Guided Dialogue dataset, and all 3 languages of the Facebook multilingual dialog datasets. Our models also demonstrate strong zero- and few-shot performance, reaching over 75% accuracy using only 100 training examples in all datasets.

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SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning
Chenrui Guo | Haoran Cui | Li Zhang | Jiamin Wang | Wei Lu | Jian Wu
Proceedings of the 8th International Workshop on Mining Scientific Publications

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers from the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from ~33.3k PMC papers in the CORD-19 dataset, released on June 13, 2020. We will provide continuous supplementary data contribution to the CORD-19 and other datasets. The source code is released at https://gitee.com/irlab/SmartCiteCon.

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Small but Mighty: New Benchmarks for Split and Rephrase
Li Zhang | Huaiyu Zhu | Siddhartha Brahma | Yunyao Li
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones. As a relatively new task, it is paramount to ensure the soundness of its evaluation benchmark and metric. We find that the widely used benchmark dataset universally contains easily exploitable syntactic cues caused by its automatic generation process. Taking advantage of such cues, we show that even a simple rule-based model can perform on par with the state-of-the-art model. To remedy such limitations, we collect and release two crowdsourced benchmark datasets. We not only make sure that they contain significantly more diverse syntax, but also carefully control for their quality according to a well-defined set of criteria. While no satisfactory automatic metric exists, we apply fine-grained manual evaluation based on these criteria using crowdsourcing, showing that our datasets better represent the task and are significantly more challenging for the models.

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Reasoning about Goals, Steps, and Temporal Ordering with WikiHow
Li Zhang | Qing Lyu | Chris Callison-Burch
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations (“learn poses” is a step in the larger goal of “doing yoga”) and step-step temporal relations (“buy a yoga mat” typically precedes “learn poses”). We introduce a dataset targeting these two relations based on wikiHow, a website of instructional how-to articles. Our human-validated test set serves as a reliable benchmark for common-sense inference, with a gap of about 10% to 20% between the performance of state-of-the-art transformer models and human performance. Our automatically-generated training set allows models to effectively transfer to out-of-domain tasks requiring knowledge of procedural events, with greatly improved performances on SWAG, Snips, and Story Cloze Test in zero- and few-shot settings.

2019

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Multi-Label Transfer Learning for Multi-Relational Semantic Similarity
Li Zhang | Steven Wilson | Rada Mihalcea
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.

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CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis
Mengting Hu | Shiwan Zhao | Li Zhang | Keke Cai | Zhong Su | Renhong Cheng | Xiaowei Shen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Aspect level sentiment classification is a fine-grained sentiment analysis task. To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various attention-based methods for generating aspect-specific sentence representations. However, the attention may inherently introduce noise and downgrade the performance. In this paper, we propose constrained attention networks (CAN), a simple yet effective solution, to regularize the attention for multi-aspect sentiment analysis, which alleviates the drawback of the attention mechanism. Specifically, we introduce orthogonal regularization on multiple aspects and sparse regularization on each single aspect. Experimental results on two public datasets demonstrate the effectiveness of our approach. We further extend our approach to multi-task settings and outperform the state-of-the-art methods.

2018

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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.

2012

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Affect Detection from Semantic Interpretation of Drama Improvisation
Li Zhang | Ming Jiang
Proceedings of COLING 2012: Posters

2010

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Metaphor Interpretation and Context-based Affect Detection
Li Zhang
Coling 2010: Posters

2007

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Don’t worry about metaphor: affect detection for conversational agents
Catherine Smith | Timothy Rumbell | John Barnden | Robert Hendley | Mark Lee | Alan Wallington | Li Zhang
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

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Exploitation in Affect Detection in Open-Ended Improvisational Text
Li Zhang | John A. Barnden | Robert J. Hendley | Alan M. Wallington
Proceedings of the Workshop on Sentiment and Subjectivity in Text

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Empirical Study on the Performance Stability of Named Entity Recognition Model across Domains
Hong Lei Guo | Li Zhang | Zhong Su
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Developments in Affect Detection in E-drama
Li Zhang | John A. Barnden | Robert J. Hendley | Alan M. Wallington
Demonstrations