Papers by Anuj Goyal
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents (D19-56)
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| Challenge: | Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. |
| Approach: | They propose to use conditional variational auto-encoders to augment training data of a popular commercial artificial agent with a small set of phrase templates to generate new semantically similar phrases. |
| Outcome: | The proposed approach outperforms the previous controlled text generation techniques with limited data and significantly outperformed the previous methods. |
Semantic Span Annotation: An Exploratory Study of LLM Annotation (2026.acl-srw)
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| Challenge: | Structured span extraction research is siloed by context length, annotation task, and domain . Identifying a span within a natural language text and affixing it with a semantic label has been considered a core task in NLP . |
| Approach: | They propose a framework for structured span annotation that integrates five datasets under a common JSONL format with character-level offsets. |
| Outcome: | The proposed framework can generalize across four domains under three prompting configurations. |
Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs (2023.acl-industry)
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Abishek Komma, Nagesh Panyam Chandrasekarasastry, Timothy Leffel, Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas, Aram Galstyan
| Challenge: | Existing methods to dialog quality estimation focus on evaluating individual turns or collecting dialog-level quality measurements from end users immediately following an interaction. |
| Approach: | They propose a dialog-level annotation workflow called Dialog Quality Annotation . they propose to annotate dialogs for attributes such as goal completion and user sentiment . |
| Outcome: | The proposed model outperforms existing methods for dialog quality estimation . it shows that high-quality human-annotated data is important for dialog-quality evaluation . |
Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)
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| Challenge: | Knowledge graph based simple question answering is a major area of research in question answering. |
| Approach: | They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches. |
| Outcome: | The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset. |
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
| Approach: | They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations . |
| Outcome: | The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient . |
MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines (2020.lrec-1)
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
| Challenge: | MultiWOZ 2.0 has substantial noise in dialogue state annotations and dialogue utterances . follow-up work has augmented the original dataset with user dialogue acts . |
| Approach: | They propose to reannotate dialogue state and utterances based on original dataset . they then compare their results to other datasets to improve their models . |
| Outcome: | The proposed dataset improves on the noise in the dialogue state annotations and dialogue utterances. |
OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation (2021.naacl-industry)
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| Challenge: | Existing models for OOD detection work with text, but they do not work directly with the text. |
| Approach: | They propose to use a sequential generative adversarial network (SeqGAN) based model to generate OOD data for a given domain automatically. |
| Outcome: | The proposed model outperforms state-of-the-art in OOD detection metrics for ROSTD and OSQ datasets. |