193 citations · 1.5k across the 86 of their papers we have counts for
89 papers
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation
Jie He, Tao Wang, Deyi Xiong +1
Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of…
Uncovering Customer Issues through Topological Natural Language Analysis
Shu-Ting Pi, Sidarth Srinivasan, Yuying Zhu +2
E-commerce companies deal with a high volume of customer service requests daily. While a simple annotation system is often used to summarize the topics of customer contacts, thorou…
Universal Model in Online Customer Service
Shu-Ting Pi, Cheng-Ping Hsieh, Qun Liu +1
Building machine learning models can be a time-consuming process that often takes several months to implement in typical business scenarios. To ensure consistent model performance…
Teacher-Student Learning on Complexity in Intelligent Routing
Shu-Ting Pi, Michael Yang, Yuying Zhu +1
Customer service is often the most time-consuming aspect for e-commerce websites, with each contact typically taking 10-15 minutes. Effectively routing customers to appropriate age…
Contact Complexity in Customer Service
Shu-Ting Pi, Michael Yang, Qun Liu
Customers who reach out for customer service support may face a range of issues that vary in complexity. Routing high-complexity contacts to junior agents can lead to multiple tran…
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification
Min Li, Hao Zhou, Qun Liu +2
In classification problems, the datasets are usually imbalanced, noisy or complex. Most sampling algorithms only make some improvements to the linear sampling mechanism of the synt…
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Chuang Liu, Renren Jin, Yuqi Ren +10
Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capabil…
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions
Hao Sun, Zhexin Zhang, Fei Mi +6
Morality in dialogue systems has raised great attention in research recently. A moral dialogue system aligned with users' values could enhance conversation engagement and user conn…
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages
Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo +6
MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval…
ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset
Zhihua Jin, Xingbo Wang, Furui Cheng +3
Benchmark datasets play an important role in evaluating Natural Language Understanding (NLU) models. However, shortcuts -- unwanted biases in the benchmark datasets -- can damage t…
Universal Conditional Masked Language Pre-training for Neural Machine Translation
Pengfei Li, Liangyou Li, Meng Zhang +2
Pre-trained sequence-to-sequence models have significantly improved Neural Machine Translation (NMT). Different from prior works where pre-trained models usually adopt an unidirect…
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021
Xingshan Zeng, Liangyou Li, Qun Liu
This paper describes the system submitted to the IWSLT 2021 Multilingual Speech Translation (MultiST) task from Huawei Noah's Ark Lab. We use a unified transformer architecture for…
Learning Multilingual Representation for Natural Language Understanding with Enhanced Cross-Lingual Supervision
Yinpeng Guo, Liangyou Li, Xin Jiang +1
Recently, pre-training multilingual language models has shown great potential in learning multilingual representation, a crucial topic of natural language processing. Prior works g…
RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer
Xingshan Zeng, Liangyou Li, Qun Liu
End-to-end simultaneous speech translation (SST), which directly translates speech in one language into text in another language in real-time, is useful in many scenarios but has n…
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management
Silin Gao, Ryuichi Takanobu, Wei Peng +2
Task-oriented dialog (TOD) systems typically manage structured knowledge (e.g. ontologies and databases) to guide the goal-oriented conversations. However, they fall short of handl…
Improved OOD Generalization via Adversarial Training and Pre-training
Mingyang Yi, Lu Hou, Jiacheng Sun +4
Recently, learning a model that generalizes well on out-of-distribution (OOD) data has attracted great attention in the machine learning community. In this paper, after defining OO…
PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
Wei Zeng, Xiaozhe Ren, Teng Su +35
Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demon…
Extract then Distill: Efficient and Effective Task-Agnostic BERT Distillation
Cheng Chen, Yichun Yin, Lifeng Shang +4
Task-agnostic knowledge distillation, a teacher-student framework, has been proved effective for BERT compression. Although achieving promising results on NLP tasks, it requires en…
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables
Krtin Kumar, Peyman Passban, Mehdi Rezagholizadeh +2
Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens.\footnote{In this…
An Approach to Improve Robustness of NLP Systems against ASR Errors
Tong Cui, Jinghui Xiao, Liangyou Li +2
Speech-enabled systems typically first convert audio to text through an automatic speech recognition (ASR) model and then feed the text to downstream natural language processing (N…
Dependency Graph-to-String Statistical Machine Translation
Liangyou Li, Andy Way, Qun Liu
We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntac…
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Mingyang Yi, Lu Hou, Lifeng Shang +3
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…
LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching
Qun Liu, Matthew Shreve, Raja Bala
Although data is abundant, data labeling is expensive. Semi-supervised learning methods combine a few labeled samples with a large corpus of unlabeled data to effectively train mod…
From Unsupervised Machine Translation To Adversarial Text Generation
Ahmad Rashid, Alan Do-Omri, Md. Akmal Haidar +2
We present a self-attention based bilingual adversarial text generator (B-GAN) which can learn to generate text from the encoder representation of an unsupervised neural machine tr…