About Me

Hi! I am a research scientist at Salesforce Research. I develop novel machine learning solutions for natural language understanding tasks such as semantic parsing and question answering. In addition, I am broadly interested in leveraging AI to help people become more productive in professional and everyday life.

Previously I was a graduate student at the Paul G. Allen School of Computer Science & Engineering, University of Washington, working with Luke Zettlemoyer and Michael D. Ernst on data-driven natural language programming. We develop natural language based programming assistant tools for non-expert programmers by automatically learning from experts and the web.

Research Highlights

Tellina Logo

Tellina is an end-user scripting assistant that can be queried in natural language. It translates a natural language sentence typed by the user into a piece of short, executable script. The underlying models are neural encoder-decoders trained on text-script pairs. The current prototype is instantiated in the Bash domain.
This work poses several challenges including scalable data collection, never-ending online learning and personalization, most of which are common in all practical semantic parsing problems. [LREC'18, UW-CSE-TR'17]

Publications

Conference Proceedings

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 and Dragomir Radev
EMNLP 2019.
PDF Abstract Bibtex Leaderboard
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.
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 and Dragomir Radev.
EMNLP 2019.
PDF Abstract Bibtex Code
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.
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.
ACL 2019.
PDF Abstract Bibtex Leaderboard
We present SParC, a dataset for cross-domain Semantic Parsing in Context. It consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries), 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 (1) demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to new 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 than 10% over all interaction sequences, indicating that the cross-domain setting and the contextual phenomena of the dataset present significant challenges for future research.
Multi-Hop Knowledge Graph Reasoning with Reward Shaping.
Xi Victoria Lin, Richard Socher and Caiming Xiong.
EMNLP 2018.
PDF Abstract Bibtex Talk Slides Press Code
Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used for training, the agent can be misled by spurious search trajectories that incidentally lead to the correct answer. We propose two modeling advances to address both issues: (1) we reduce the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts; (2) we counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks. Our approach significantly improves over existing path-based KGQA models on several benchmark datasets and is comparable or better than embedding-based models.
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System.
Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer and Michael D. Ernst.
LREC 2018.
PDF Abstract Bibtex Dataset & Code
We present new data and semantic parsing methods for the problem of mapping english sentences to Bash commands (NL2Bash). Our long-term goal is to enable any user to easily solve otherwise repetitive tasks (such as file manipulation, search, and application-specific scripting) by simply stating their intents in English. We take a first step in this domain, by providing a large new dataset of challenging but commonly used commands paired with their English descriptions, along with the baseline methods to establish performance levels on this task.
Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text.
Kristina Toutanova, Xi Victoria Lin, Scott Wen-tau Yih, Hoifung Poon and Chris Quirk.
ACL 2016.
PDF Abstract Bibtex
Modeling relation paths has offered significant gains in embedding models for knowledge base (KB) completion. However, enumerating paths between two entities is very expensive, and existing approaches typically resort to approximation with a sampled subset. This problem is particularly acute when text is jointly modeled with KB relations and used to provide direct evidence for facts mentioned in it. In this paper, we propose the first exact dynamic programming algorithm which enables efficient incorporation of all relation paths of bounded length, while modeling both relation types and intermediate nodes in the compositional path representations. We conduct a theoretical analysis of the efficiency gain from the approach. Experiments on two datasets show that it addresses representational limitations in prior approaches and improves accuracy in KB completion.

Workshop Proceedings & Technical Reports

Program Synthesis from Natural Language Using Recurrent Neural Networks.
Xi Victoria Lin, Chenglong Wang, Deric Pang, Kevin Vu, Luke Zettlemoyer, Michael D. Ernst.
University of Washington Department of Computer Science and Engineering Technical Report 2017.
PDF Abstract Bibtex Tellina Tool
Even if a competent programmer knows what she wants to do and can describe it in English, it can still be difficult to write code to achieve the goal. Existing resources, such as question-and-answer websites, tabulate specific operations that someone has wanted to perform in the past, but they are not effective in generalizing to new tasks, to compound tasks that require combining previous questions, or sometimes even to variations of listed tasks.

Our goal is to make programming easier and more productive by letting programmers use their own words and concepts to express the intended operation, rather than forcing them to accommodate the machine by memorizing its grammar. We have built a system that lets a programmer describe a desired operation in natural language, then automatically translates it to a programming language for review and approval by the programmer. Our system, Tellina, does the translation using recurrent neural networks (RNNs), a state-of-the-art natural language processing technique that we augmented with slot (argument) filling and other enhancements.

We evaluated Tellina in the context of shell scripting. We trained Tellina's RNNs on textual descriptions of file system operations and bash one-liners, scraped from the web. Although recovering completely correct commands is challenging, Tellina achieves top-3 accuracy of 80% for producing the correct command structure. In a controlled study, programmers who had access to Tellina outperformed those who did not, even when Tellina's predictions were not completely correct, to a statistically significant degree.
Multi-label Learning with Posterior Regularization.
Xi Victoria Lin, Sameer Singh, Luheng He, Ben Taskar, and Luke Zettlemoyer.
NeurIPS Workshop on Modern Machine Learning and NLP 2014.
PDF Abstract Bibtex
In many multi-label learning problems, especially as the number of labels grow, it is challenging to gather completely annotated data. This work presents a new approach for multi-label learning from incomplete annotations. The main assumption is that because of label correlation, the true label matrix as well as the soft predictions of classifiers shall be approximately low rank. We introduce a posterior regularization technique which enforces soft constraints on the classifiers, regularizing them to prefer sparse and low-rank predictions. Avoiding strict low-rank constraints results in classifiers which better fit the real data. The model can be trained efficiently using EM and stochastic gradient descent. Experiments in both the image and text domains demonstrate the contributions of each modeling assumption and show that the proposed approach achieves state-of-the-art performance on a number of challenging datasets..

Professional Services

    Organizing Committee
2020
INT‑EX 
    Reviewer
2020 2019 2018 2017 2016 2015
ICML 
ACL 
EMNLP 
NAACL 
COLING 
CoNLL 

Miscellaneous

    I was a PhD student of the late Ben Taskar.
    The Taskar Center for Accessible Technology (TCAT) was lauched by Anat Caspi in November, 2014. I am excited about its mission. Anat's expertise and unique perspective would lead to accessible technologies that could change the life for many.
    I'm fascinated by different kinds of puzzles. At some point I tried to make a few: Sea Virus, Chocolate Crush.