Search Results for author: Rajen Subba

Found 14 papers, 7 papers with code

Zero-Shot Dialogue State Tracking via Cross-Task Transfer

1 code implementation EMNLP 2021 Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung

Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.

Dialogue State Tracking Question Answering +1

Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking

1 code implementation10 May 2021 Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.

Dialogue State Tracking Transfer Learning

Situated and Interactive Multimodal Conversations

2 code implementations COLING 2020 Seungwhan Moon, Satwik Kottur, Paul A. Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard

Next generation virtual assistants are envisioned to handle multimodal inputs (e. g., vision, memories of previous interactions, in addition to the user's utterances), and perform multimodal actions (e. g., displaying a route in addition to generating the system's utterance).

Response Generation

SIMMC: Situated Interactive Multi-Modal Conversational Data Collection And Evaluation Platform

no code implementations7 Nov 2019 Paul A. Crook, Shivani Poddar, Ankita De, Semir Shafi, David Whitney, Alborz Geramifard, Rajen Subba

To this end, we introduce SIMMC, an extension to ParlAI for multi-modal conversational data collection and system evaluation.

Unity

Memory Graph Networks for Explainable Memory-grounded Question Answering

no code implementations CONLL 2019 Seungwhan Moon, Pararth Shah, Anuj Kumar, Rajen Subba

We introduce Episodic Memory QA, the task of answering personal user questions grounded on memory graph (MG), where episodic memories and related entity nodes are connected via relational edges.

Question Answering

Memory Grounded Conversational Reasoning

no code implementations IJCNLP 2019 Seungwhan Moon, Pararth Shah, Rajen Subba, Anuj Kumar

To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes.

A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog

no code implementations WS 2019 Jinfeng Rao, Kartikeya Upasani, Anusha Balakrishnan, Michael White, Anuj Kumar, Rajen Subba

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.

OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs

no code implementations ACL 2019 Seungwhan Moon, Pararth Shah, Anuj Kumar, Rajen Subba

We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes.

Knowledge Graphs

Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

1 code implementation ACL 2019 Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba

Generating fluent natural language responses from structured semantic representations is a critical step in task-oriented conversational systems.

Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems

1 code implementation NAACL 2019 Ashwini Challa, Kartikeya Upasani, Anusha Balakrishnan, Rajen Subba

While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don't correctly capture the distribution of errors that data-driven generators are likely to make.

Classification General Classification +2

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