Search Results for author: Anjali Narayan-Chen

Found 6 papers, 2 papers with code

TEACh: Task-driven Embodied Agents that Chat

1 code implementation1 Oct 2021 Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur, Dilek Hakkani-Tur

Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.

Dialogue Understanding

Style Control for Schema-Guided Natural Language Generation

no code implementations EMNLP (NLP4ConvAI) 2021 Alicia Y. Tsai, Shereen Oraby, Vittorio Perera, Jiun-Yu Kao, Yuheng Du, Anjali Narayan-Chen, Tagyoung Chung, Dilek Hakkani-Tur

Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods.

Pretrained Language Models Task-Oriented Dialogue Systems +1

Learning to execute instructions in a Minecraft dialogue

no code implementations ACL 2020 Prashant Jayannavar, Anjali Narayan-Chen, Julia Hockenmaier

The Minecraft Collaborative Building Task is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment.

Learning to Execute

Schema-Guided Natural Language Generation

1 code implementation INLG (ACL) 2020 Yuheng Du, Shereen Oraby, Vittorio Perera, Minmin Shen, Anjali Narayan-Chen, Tagyoung Chung, Anu Venkatesh, Dilek Hakkani-Tur

We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity.

Text Generation

Collaborative Dialogue in Minecraft

no code implementations ACL 2019 Anjali Narayan-Chen, Prashant Jayannavar, Julia Hockenmaier

We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios.

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