Angel Chang

Also published as: Angel X. Chang


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Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous EnvironmentsLAW) Supervision for Vision-and-Language Navigation in Continuous Environments
Sonia Raychaudhuri | Saim Wani | Shivansh Patel | Unnat Jain | Angel Chang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle ‘off the path’ scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent’s location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.


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Mimic and Rephrase : Reflective Listening in Open-Ended Dialogue
Justin Dieter | Tian Wang | Arun Tejasvi Chaganty | Gabor Angeli | Angel X. Chang
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Reflective listeningdemonstrating that you have heard your conversational partneris key to effective communication. Expert human communicators often mimic and rephrase their conversational partner, e.g., when responding to sentimental stories or to questions they do n’t know the answer to. We introduce a new task and an associated dataset wherein dialogue agents similarly mimic and rephrase a user’s request to communicate sympathy (I’m sorry to hear that) or lack of knowledge (I do not know that). We study what makes a rephrasal response good against a set of qualitative metrics. We then evaluate three models for generating responses : a syntax-aware rule-based system, a seq2seq LSTM neural models with attention (S2SA), and the same neural model augmented with a copy mechanism (S2SA+C). In a human evaluation, we find that S2SA+C and the rule-based system are comparable and approach human-generated response quality. In addition, experiences with a live deployment of S2SA+C in a customer support setting suggest that this generation task is a practical contribution to real world conversational agents.


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A Two-stage Sieve Approach for Quote Attribution
Grace Muzny | Michael Fang | Angel Chang | Dan Jurafsky
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset : QuoteLi3. Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our two-stage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune for overall performance or higher precision, which is useful for many real-world use cases. Our system achieves an average F-score of 87.5 across three novels, outperforming previous systems, and can be tuned for precision of 90.4 at a recall of 65.1.