Nancy F. Chen

Dynamic Sliding Window Modeling for Abstractive Meeting Summarization

2022 · Interspeech 2022

International audience

Towards Summary Candidates Fusion

2022 · arXiv (Cornell University)

Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown that they have not reached their full potential, with a wide gap …

SNIPER Training: Single-Shot Sparse Training for Text-to-Speech

2022 · arXiv (Cornell University)

Text-to-speech (TTS) models have achieved remarkable naturalness in recent years, yet like most deep neural models, they have more parameters than necessary. Sparse TTS models can improve on dense models via pruning and extra retraining, …

Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation

2023 · arXiv (Cornell University)

Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former …

PromptSum: Parameter-Efficient Controllable Abstractive Summarization

2023 · arXiv (Cornell University)

Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios. However, …

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation

2023 · arXiv (Cornell University)

Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many …

Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions

2024 · arXiv (Cornell University)

Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education. With the emergence of generative artificial intelligence, large language …

Improving non-native mispronunciation detection and enriching diagnostic feedback with DNN-based speech attribute modeling

2016

We propose the use of speech attributes, such as voicing and aspiration, to address two key research issues in computer assisted pronunciation training (CAPT) for L2 learners, namely detecting mispronunciation and providing diagnostic feedback. To …