activity
20142025
most citedIncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

58 citations · 182 across the 13 of their papers we have counts for

collaborators

13 papers

cs.LG2025

Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models

Lior Belenki, Alekh Agarwal, Tianze Shi +1

We propose a method to optimize language model pre-training data mixtures through efficient approximation of the cross-entropy loss corresponding to each candidate mixture via a Mi…

cs.CL2021

Diversity-Aware Batch Active Learning for Dependency Parsing

Tianze Shi, Adrian Benton, Igor Malioutov +1

While the predictive performance of modern statistical dependency parsers relies heavily on the availability of expensive expert-annotated treebank data, not all annotations contri…

cs.CL2021

Learning Syntax from Naturally-Occurring Bracketings

Tianze Shi, Ozan İrsoy, Igor Malioutov +1

Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundari…

cs.CL2020★ 4 cited

On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

Tianze Shi, Chen Zhao, Jordan Boyd-Graber +2

Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the u…

cs.CL2020★ 1 cited

Semantic Role Labeling as Syntactic Dependency Parsing

Tianze Shi, Igor Malioutov, Ozan İrsoy

We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three…

cs.CL2020

Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches

Tianze Shi, Lillian Lee

An interesting and frequent type of multi-word expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named…

cs.CL2019★ 44 cited

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

Tao Yu, Rui Zhang, He Yang Er +21

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 f…

cs.CL2019★ 49 cited

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

Rui Zhang, Tao Yu, He Yang Er +7

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…

cs.CL2018★ 58 cited

IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

Tianze Shi, Kedar Tatwawadi, Kaushik Chakrabarti +3

We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inv…

cs.CL2018★ 1 cited

Global Transition-based Non-projective Dependency Parsing

Carlos Gómez-Rodríguez, Tianze Shi, Lillian Lee

Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and Chinese dependency parsing by combining dynamic-programming implementations of transition-based depende…

cs.CL2018

Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers

Tianze Shi, Carlos Gómez-Rodríguez, Lillian Lee

We generalize Cohen, Gómez-Rodríguez, and Satta's (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. This inc…

cs.CL2017★ 9 cited

Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set

Tianze Shi, Liang Huang, Lillian Lee

We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of…

cs.CL2014★ 16 cited

Linking GloVe with word2vec

Tianze Shi, Zhiyuan Liu

The Global Vectors for word representation (GloVe), introduced by Jeffrey Pennington et al. is reported to be an efficient and effective method for learning vector representations…