1 code implementation • 16 Sep 2022 • Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein
Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation: neural language modeling and rule-based generation.
no code implementations • WS 2019 • Kartikeya Upasani, David King, Jinfeng Rao, Anusha Balakrishnan, Michael White
We describe our exploratory system for the shallow surface realization task, which combines morphological inflection using character sequence-to-sequence models with a baseline linearizer that implements a tree-to-tree model using sequence-to-sequence models on serialized trees.
no code implementations • WS 2019 • Jinfeng Rao, Kartikeya Upasani, Anusha Balakrishnan, Michael White, Anuj Kumar, Rajen Subba
Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.
1 code implementation • IJCNLP 2019 • Dongyeop Kang, Anusha Balakrishnan, Pararth Shah, Paul Crook, Y-Lan Boureau, Jason Weston
These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.
1 code implementation • ACL 2019 • Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba
Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.
1 code implementation • NAACL 2019 • Ashwini Challa, Kartikeya Upasani, Anusha Balakrishnan, Rajen Subba
While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don't correctly capture the distribution of errors that data-driven generators are likely to make.
3 code implementations • EMNLP 2018 • He He, Derek Chen, Anusha Balakrishnan, Percy Liang
We consider negotiation settings in which two agents use natural language to bargain on goods.
no code implementations • 25 Nov 2017 • Pranav Rajpurkar, Vinaya Polamreddi, Anusha Balakrishnan
We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household.
2 code implementations • ACL 2017 • He He, Anusha Balakrishnan, Mihail Eric, Percy Liang
To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.