Balakrishnan Narayanaswamy


2020

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Recursive Template-based Frame Generation for Task Oriented Dialog
Rashmi Gangadharaiah | Balakrishnan Narayanaswamy
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The Natural Language Understanding (NLU) component in task oriented dialog systems processes a user’s request and converts it into structured information that can be consumed by downstream components such as the Dialog State Tracker (DST). This information is typically represented as a semantic frame that captures the intent and slot-labels provided by the user. We first show that such a shallow representation is insufficient for complex dialog scenarios, because it does not capture the recursive nature inherent in many domains. We propose a recursive, hierarchical frame-based representation and show how to learn it from data. We formulate the frame generation task as a template-based tree decoding task, where the decoder recursively generates a template and then fills slot values into the template. We extend local tree-based loss functions with terms that provide global supervision and show how to optimize them end-to-end. We achieve a small improvement on the widely used ATIS dataset and a much larger improvement on a more complex dataset we describe here.

2019

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Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog
Rashmi Gangadharaiah | Balakrishnan Narayanaswamy
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification. In many real-world scenarios, users have multiple intents in the same utterance, and a token-level slot label can belong to more than one intent. We investigate an attention-based neural network model that performs multi-label classification for identifying multiple intents and produces labels for both intents and slot-labels at the token-level. We show state-of-the-art performance for both intent detection and slot-label identification by comparing against strong, recently proposed models. Our model provides a small but statistically significant improvement of 0.2% on the predominantly single-intent ATIS public data set, and 55% intent accuracy improvement on an internal multi-intent dataset.

2018

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What we need to learn if we want to do and not just talk
Rashmi Gangadharaiah | Balakrishnan Narayanaswamy | Charles Elkan
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

In task-oriented dialog, agents need to generate both fluent natural language responses and correct external actions like database queries and updates. Our paper makes the first attempt at evaluating state of the art models on a large real world task with human users. We show that methods that achieve state of the art performance on synthetic datasets, perform poorly in real world dialog tasks. We propose a hybrid model, where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. The hybrid model on the customer support data achieves a 78% relative improvement in fluency, and a 200% improvement in accuracy of external calls.

2014

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Learning to Re-rank for Interactive Problem Resolution and Query Refinement
Rashmi Gangadharaiah | Balakrishnan Narayanaswamy | Charles Elkan
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

2013

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Natural Language Query Refinement for Problem Resolution from Crowd-Sourced Semi-Structured Data
Rashmi Gangadharaiah | Balakrishnan Narayanaswamy
Proceedings of the Sixth International Joint Conference on Natural Language Processing