Search Results for author: Tong Niu

Found 14 papers, 6 papers with code

OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval

no code implementations Findings (ACL) 2022 Tong Niu, Kazuma Hashimoto, Yingbo Zhou, Caiming Xiong

When finetuned on a single rich-resource language pair, be it English-centered or not, our model is able to match the performance of the ones finetuned on all language pairs under the same data budget with less than 2. 0 points decrease in accuracy.

Machine Translation Zero-Shot Cross-Lingual Transfer

CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers

1 code implementation ICLR 2021 Shiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu, Nazneen Rajani, Xifeng Yan, Yingbo Zhou, Caiming Xiong

Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.

Ranked #2 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)

Dialogue State Tracking Multi-domain Dialogue State Tracking

Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality

no code implementations Findings (EMNLP) 2021 Gustavo Aguilar, Bryan McCann, Tong Niu, Nazneen Rajani, Nitish Keskar, Thamar Solorio

To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT.

Unsupervised Paraphrasing with Pretrained Language Models

no code implementations EMNLP 2021 Tong Niu, Semih Yavuz, Yingbo Zhou, Nitish Shirish Keskar, Huan Wang, Caiming Xiong

To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step.

Language Modelling Paraphrase Generation +2

Explaining and Improving Model Behavior with k Nearest Neighbor Representations

no code implementations18 Oct 2020 Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, Caiming Xiong

Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens.

Natural Language Inference

AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses

no code implementations15 Jan 2020 Tong Niu, Mohit Bansal

In our work, we build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering.

Feature Engineering

Automatically Learning Data Augmentation Policies for Dialogue Tasks

1 code implementation IJCNLP 2019 Tong Niu, Mohit Bansal

Automatic data augmentation (AutoAugment) (Cubuk et al., 2019) searches for optimal perturbation policies via a controller trained using performance rewards of a sampled policy on the target task, hence reducing data-level model bias.

Data Augmentation Dialogue Generation +2

Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression

no code implementations7 Sep 2019 Tong Niu, Caiming Xiong, Richard Socher

In this work, we propose a fully unsupervised model, Deleter, that is able to discover an "optimal deletion path" for an arbitrary sentence, where each intermediate sequence along the path is a coherent subsequence of the previous one.

Language Modelling Reading Comprehension +2

A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting

no code implementations30 May 2019 Pei Du, Jianzhou Wang, Yan Hao, Tong Niu, Wendong Yang

Next, a new multi-objective algorithm called MOHHO is first developed in this study, which are introduced to tune the parameters of ELM model with high forecasting accuracy and stability for air pollution series prediction, simultaneously.

Air Pollution Prediction Time Series Prediction

Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models

1 code implementation CONLL 2018 Tong Niu, Mohit Bansal

We present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies that evaluate over-sensitivity to small and semantics-preserving edits, as well as Should-Change strategies that test if a model is over-stable against subtle yet semantics-changing modifications.

Polite Dialogue Generation Without Parallel Data

1 code implementation TACL 2018 Tong Niu, Mohit Bansal

We present three weakly-supervised models that can generate diverse polite (or rude) dialogue responses without parallel data.

Dialogue Generation Language Modelling +1

Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models

no code implementations NAACL 2018 Sweta Karlekar, Tong Niu, Mohit Bansal

More importantly, we next interpret what these neural models have learned about the linguistic characteristics of AD patients, via analysis based on activation clustering and first-derivative saliency techniques.

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