Saim Wani
2021
Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments
Sonia Raychaudhuri
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Saim Wani
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Shivansh Patel
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Unnat Jain
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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.
An End-to-End Network for Emotion-Cause Pair Extraction
Aaditya Singh
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Shreeshail Hingane
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Saim Wani
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Ashutosh Modi
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (∼ 6.5% increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.
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Co-authors
- Sonia Raychaudhuri 1
- Shivansh Patel 1
- Unnat Jain 1
- Angel Chang 1
- Aaditya Singh 1
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