93 citations · 526 across the 105 of their papers we have counts for
21 papers · 1 filter
SAIL: Search-Augmented Instruction Learning
Hongyin Luo, Yung-Sung Chuang, Yuan Gong +6
Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and informatio…
Improving Rare Words Recognition through Homophone Extension and Unified Writing for Low-resource Cantonese Speech Recognition
HoLam Chung, Junan Li, Pengfei Liu1 +3
Homophone characters are common in tonal syllable-based languages, such as Mandarin and Cantonese. The data-intensive end-to-end Automatic Speech Recognition (ASR) systems are more…
Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation
Liping Tang, Zhen Li, Zhiquan Luo +1
This paper investigates an unsupervised approach towards deriving a universal, cross-lingual word embedding space, where words with similar semantics from different languages are c…
Cross-lingual Word Embeddings in Hyperbolic Space
Chandni Saxena, Mudit Chaudhary, Helen Meng
Cross-lingual word embeddings can be applied to several natural language processing applications across multiple languages. Unlike prior works that use word embeddings based on the…
An End-to-end Chinese Text Normalization Model based on Rule-guided Flat-Lattice Transformer
Wenlin Dai, Changhe Song, Xiang Li +4
Text normalization, defined as a procedure transforming non standard words to spoken-form words, is crucial to the intelligibility of synthesized speech in text-to-speech system. R…
A Character-level Span-based Model for Mandarin Prosodic Structure Prediction
Xueyuan Chen, Changhe Song, Yixuan Zhou +4
The accuracy of prosodic structure prediction is crucial to the naturalness of synthesized speech in Mandarin text-to-speech system, but now is limited by widely-used sequence-to-s…
Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks
Jingyan Zhou, Jiawen Deng, Fei Mi +6
The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems…
Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding
Jingyan Zhou, Xiaohan Feng, King Keung Wu +1
Popular approaches for Natural Language Understanding (NLU) usually rely on a huge amount of annotated data or handcrafted rules, which is laborious and not adaptive to domain exte…
Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition
Pengfei Liu, Kun Li, Helen Meng
Emotion recognition is a challenging and actively-studied research area that plays a critical role in emotion-aware human-computer interaction systems. In a multimodal setting, tem…
Mixed Precision Low-bit Quantization of Neural Network Language Models for Speech Recognition
Junhao Xu, Jianwei Yu, Shoukang Hu +2
State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive…
Partner Personas Generation for Diverse Dialogue Generation
Hongyuan Lu, Wai Lam, Hong Cheng +1
Incorporating personas information allows diverse and engaging responses in dialogue response generation. Unfortunately, prior works have primarily focused on self personas and hav…
Countering Online Hate Speech: An NLP Perspective
Mudit Chaudhary, Chandni Saxena, Helen Meng
Online hate speech has caught everyone's attention from the news related to the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity - an umbrella term for onli…
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling
Pengfei Liu, Kun Li, Helen Meng
User queries for a real-world dialog system may sometimes fall outside the scope of the system's capabilities, but appropriate system responses will enable smooth processing throug…
Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing
Pengfei Liu, Youzhang Ning, King Keung Wu +2
Intent understanding plays an important role in dialog systems, and is typically formulated as a supervised learning problem. However, it is challenging and time-consuming to desig…
Enhancing Word-Level Semantic Representation via Dependency Structure for Expressive Text-to-Speech Synthesis
Yixuan Zhou, Changhe Song, Jingbei Li +4
Exploiting rich linguistic information in raw text is crucial for expressive text-to-speech (TTS). As large scale pre-trained text representation develops, bidirectional encoder re…
Bayesian Transformer Language Models for Speech Recognition
Boyang Xue, Jianwei Yu, Junhao Xu +6
State-of-the-art neural language models (LMs) represented by Transformers are highly complex. Their use of fixed, deterministic parameter estimates fail to account for model uncert…
Unstructured Knowledge Access in Task-oriented Dialog Modeling using Language Inference, Knowledge Retrieval and Knowledge-Integrative Response Generation
Mudit Chaudhary, Borislav Dzodzo, Sida Huang +10
Dialog systems enriched with external knowledge can handle user queries that are outside the scope of the supporting databases/APIs. In this paper, we follow the baseline provided…
Syntactic representation learning for neural network based TTS with syntactic parse tree traversal
Changhe Song, Jingbei Li, Yixuan Zhou +2
Syntactic structure of a sentence text is correlated with the prosodic structure of the speech that is crucial for improving the prosody and naturalness of a text-to-speech (TTS) s…
Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speech
Xu Li, Xixin Wu, Xunying Liu +1
Second language (L2) speech is often labeled with the native, phone categories. However, in many cases, it is difficult to decide on a categorical phone that an L2 segment belongs…
Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks
Xingchen Song, Guangsen Wang, Zhiyong Wu +4
Self-attention network (SAN) can benefit significantly from the bi-directional representation learning through unsupervised pretraining paradigms such as BERT and XLNet. In this pa…
Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition
Xin Zheng, Zhiyong Wu, Helen Meng +2
In this paper, we first present a new variant of Gaussian restricted Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann machine (MGRBM), with its definition…