Arun Tejasvi Chaganty

Google

Senior Research Scientist Dec 2020–(today)

• Created dialog inpainting (co-first author), a technique to generate millions of information-seeking conversations from documents using language models (T5 S–XXL). Implemented the entire bulk inference pipeline (average throughput of ~3k inference calls/s) using Apache Beam. Led human evaluation and safety analysis. Trained masked language models and retrieval models.
• Created the Conversational Playlist Curation Dataset (first author; PI), one of the first resources for conversational recommendation with multiple item ratings per-turn. Designed and implemented human-human methodology, including all annotation interfaces.
• Developed Talk the Walk (PI), a recipe to generate millions of (music) recommendation-seeking conversations from existing playlists using a combination of random walks and language models. Bootstrapped an end-to-end conversation recommendation system that significantly outperforms baselines in live experiments.
• Defined task and evaluation methodology for RARR, a post-hoc attribution and reivision method for large language models (PaLM-540B).

Research Intern Summer 2014

• Explored multi-sentence relation extraction for knowledge bases.

Square

AI Lead May 2019–Dec 2020

• Led a small team of AI engineers that built Square Assistant—a chatbot we launched in October 2019 that helps customers book and reschedule appointments with Square merchants.
• Designed and shipped conversational rescheduling feature that increased booking and rescheduling success rates by helping customers find a concrete time for their appointment; the feature understands temporal constraints in user utterances using a model-based semantic parser.
• Developed a type-safe domain-specific language to describe asynchrony and interruptions in dialog flows using coroutines. Implemented Java-to-Java compiler. DSL reduced feature code 10–20x and fixed subtle asynchrony bugs.
• Developed most of the AI model deployment, logging and data annotation infrastructure.

Eloquent Labs

Head of AI June 2018–May 2019

• Led a small team of AI engineers that built a conversational AI system for enterprise customer service. Interfaced with clients directly.
• Developed a human-in-the-loop system to fine-tune question similarity models for particular clients; led to 2–3x increases in precision and recall for each client.
• Startup acquired by Square in May 2019.

Stanford University

PhD Candidate Sept 2012–Sept 2018

• Led / part of the Stanford team at TAC-KBP 2013, 2015–17. Our entry was the top-ranked at the TAC-KBP 2015--17 Cold Start tracks.
• Co-author of CoreNLP Server, an extremely popular API server for the Stanford CoreNLP package.
• Can we scalably evaluate open-ended language tasks like information extraction or summarization with human feedback? We show fundamental limitations with existing automatic metrics (ACL 2018).
• Proposed a human-in-the-loop solution for knowledge-base population evaluation that eliminates pooling bias using a novel importance-reweighted estimator that decreases annotation costs by a factor of 4 (EMNLP 2017).
• Numeric comparisons, while common in the news, are hard to identify because their definition emerges only in context. We define an explicit representation, called a textual analogy frames, for such comparisons and build a semantic parser to identify such frames in text (EMNLP 2018).
• People best understand concepts through comparisons: we provide a system to generate compositional comparisons for numerical expressions in text, such as describing Cristiano Ronaldo's signing fee of $131 million as roughly the amount it would take to pay everyone in Kansas City the median salary for a week (ACL 2016).
• Can we efficiently learn latent variable models with guarantees? We show that this is possible for a variety of models satisfying a 'uniformly bottlenecked' assumption including discriminative mixtures of linear experts (ICML 2013), high tree-width models, log-linear models and multi-view Markov random fields (ICML 2014). In later work, we show guaranteed recovery for any mixture model with polynomial moments is possible via reduction to the generalized moment problem (NIPS 2015). All of these methods require tensor factorization, which we show can be more efficiently performed by reduction to simultaneous matrix diagonalization using random productions (AISTATS 2015).

Microsoft Research India

Intern Summer 2009, 2010, 2011

• Used dynamic analysis and concolic execution to efficiently sample from probabilistic programs by avoiding invalid states in both an importance sampling and Metropolis-Hastings setting (AISTATS 2013).
• Applied Counter-Example Guided Abstraction Refinement, and generalization (from program analysis) to the Markov Logic Network framework, with significant performance improvements over prior art (CAV 2013).