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Research Area

Conversational AI

Building software and systems that help people communicate with computers naturally, as if communicating with family and friends.

Publications

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  • Nick McKenna, Priyanka Sen
    ACL 2023 Workshop on SustaiNLP
    2023
    Popular models for Knowledge Graph Question Answering (KGQA), including semantic parsing and End-to-End (E2E) models, decode into a constrained space of KG relations. Al-though E2E models accommodate novel entities at test-time, this constraint means they cannot access novel relations, requiring expensive and time-consuming retraining whenever a new relation is added to the KG. We propose KG-Flex, a new
  • Jinheon Baek, Alham Fikri Aji, Amir Saffari
    ACL 2023 Workshop on Matching Entities
    2023
    Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowl-edge stored in parameters during pre-training. However, such internalized knowledge might be insufficient and incorrect, which could lead LLMs to generate factually wrong answers. Furthermore, fine-tuning LLMs to update their knowledge is expensive. To this end, we pro-pose
  • Recent NLP literature pays little attention to the robustness of toxicity language predictors, while these systems are most likely to be used in adversarial contexts. This paper presents a novel adversarial attack, ToxicTrap, introducing small word-level perturbations to fool SOTA text classifiers to predict toxic text samples as benign. ToxicTrap exploits greedy based search strategies to enable fast and
  • ACL Findings 2023, ACL 2023 Workshop on SustaiNLP
    2023
    Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each have advantages; however, training both model types from scratch is computationally expensive. We explore recipes to improve pre-training efficiency by initializing one model from the other. (1) Extracting the encoder from a seq2seq model, we show it underperforms a Masked Language Modeling (MLM) encoder, particularly on sequence labeling
  • Akshaya Vishnu Kudlu Shanbhogue, Ran Xue, Soumya Saha, Daniel Zhang, Ashwin Ganesan
    IWSLT 2023
    2023
    This paper describes the speech translation system submitted as part of the IWSLT 2023 shared task on low resource speech translation. The low resource task aids in building models for language pairs where the training corpus is limited. In this paper, we focus on two language pairs, namely, Tamasheq-French (Tmh→Fra) and Marathi-Hindi (Mr→Hi) and implement a speech translation system that is unconstrained

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