Papers by Tianze Shi
Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction (2021.acl-long)
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| Challenge: | Existing bubble representations encoding coordination boundaries and internal relationships are difficult to detect and parse . |
| Approach: | They propose a bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. |
| Outcome: | The proposed bubble parser beats state-of-the-art approaches on coordination structure prediction . the proposed system is based on a GENIA corpus and a Penn treebank . |
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers (N18-2)
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| Challenge: | Non-projective dependency trees account for 12.59% of all training sentences in the annotated Universal Dependencies (UD) 2.1 data. |
| Approach: | They generalize Cohen et al.'s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. |
| Outcome: | The proposed system can be extended to include a variant that reduces time complexity to O(n6), improving over the known bounds in exact inference for non-projective transition-based parsing. |
Weakly Supervised Headline Dependency Parsing (2022.findings-emnlp)
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| Challenge: | English news headlines have unique syntactic properties documented in linguistics literature since the 1930s. |
| Approach: | They propose to provide the first news headline corpus of annotated syntactic dependency trees to evaluate existing NLP parsers on news headlines. |
| Outcome: | The proposed method improves performance across different news outlets, but is moderated by constructions idiosyncratic to outlet. |
Global Transition-based Non-projective Dependency Parsing (P18-1)
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| Challenge: | Until recently, transition-based dependency parsers were limited to approximate inference due to their incompatibility with rich feature models. |
| Approach: | They propose a transition-based parser with high coverage on non-projective treebanks to support non- projective parsing. |
| Outcome: | The proposed approach is more efficient than its projective counterpart in non-projective languages. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022.emnlp-main)
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Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova
| Challenge: | Pre-trained language models struggle on out-of-distribution compositional generalization . recent work shows considerable improvements on many NLP tasks from model scaling . |
| Approach: | They evaluate encoder-decoder models up to 11B parameters and decoder-only models up 540B parameters . they compare scaling curves for fine-tuning, prompt tuning, and in-context learning methods . |
| Outcome: | The proposed scaling methods improve compositional generalization on many tasks . fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional . larger models are better at modeling the syntax of the output space, the study finds . |
Semantic Role Labeling as Syntactic Dependency Parsing (2020.emnlp-main)
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| Challenge: | Using propBank-style semantic role labeling, we reduce the task to syntactic dependency parsing. |
| Approach: | They propose to convert SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. |
| Outcome: | The proposed scheme reduces the task of (span-based) PropBank-style semantic role labeling to syntactic dependency parsing. |
Learning Syntax from Naturally-Occurring Bracketings (2021.naacl-main)
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| Challenge: | a new method for learning naturally-occurring bracketings is developed . it uses noisy and incomplete data to induce syntactic structures . |
| Approach: | They propose a partial-brackets-aware structured ramp loss in learning to address this challenge . they show that distantly-supervised models trained on naturally-occurring bracketing data are more accurate . constituency is a foundational building block for phrase-structure grammars, they argue . |
| Outcome: | The proposed model achieves an unlabeled F1 score for constituency parsing on the English WSJ corpus. |
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (D19-1)
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Rui Zhang, Tao Yu, Heyang Er, Sungrok Shim, Eric Xue, Xi Victoria Lin, Tianze Shi, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Generating SQL queries from user utterances is an important task to help end users acquire information from databases. |
| Approach: | They propose a context-dependent text-to-SQL generation task that edits previous queries . they use an utterance-table encoder and a table-aware decoder to incorporate context . |
| Outcome: | The proposed model is flexible to change individual tokens and robust to error propagation. |
Valency-Augmented Dependency Parsing (D18-1)
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| Challenge: | valency analysis is a complex task that requires a large number of subcategorizations, such as the number and types of syntactic dependents. |
| Approach: | They propose a parsing approach that explicitly models the number and types of syntactic dependents as valency patterns and a probabilistic model for tagging them. |
| Outcome: | The proposed approach outperforms the state-of-the-art labeled attachment score on 53 treebanks representing 41 languages and outperformed the previous state- of-the art labeles by 0.7. |
Diversity-Aware Batch Active Learning for Dependency Parsing (2021.naacl-main)
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| Challenge: | a high annotation cost for dependency parsers is a challenge . batch active learning (AL) is based on batch mode, which is more efficient for annotators to label in bulk. |
| Approach: | They propose to reduce the number of labeled examples needed to train a strong dependency parser using batch active learning. |
| Outcome: | The proposed approach improves on an English newswire corpus by enforcing diversity in the sampled batches. |
On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries (2020.findings-emnlp)
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| Challenge: | Large-scale semantic parsing datasets annotated with logical forms have enabled advances in supervised approaches. |
| Approach: | They propose to enrich English-language questions with SQL equivalents and alignments . they propose to use supervised attention and an auxiliary objective to disambiguate references . |
| Outcome: | The proposed method improves over strong baselines by 4.4% execution accuracy. |
Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches (2020.acl-main)
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| Challenge: | Headless multi-word expressions are frequent in natural language but lack internal syntactic dominance relations. |
| Approach: | They propose an efficient joint decoding algorithm that combines scores from both strategies. |
| Outcome: | The proposed algorithm combines scores from parsing and tagging for predicting flat MWEs . the proposed algorithm is more accurate than parse and more efficient for non-BERT features . |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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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
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)
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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu
| Challenge: | Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios. |
| Approach: | They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios. |
| Outcome: | Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift. |
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models (2025.acl-long)
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| Challenge: | Existing methods to optimize language model pre-training data mixtures are difficult due to the complexity of the data mixture. |
| Approach: | They propose a method to optimize language model pre-training data mixtures by approximating cross-entropy loss via a Mixture of Data Experts (MDE). |
| Outcome: | The proposed method improves performance on a slimPajama dataset with a mixture of data experts. |