Search Results for author: Shi Feng

Found 40 papers, 16 papers with code

Human-Centered Evaluation of Explanations

no code implementations NAACL (ACL) 2022 Jordan Boyd-Graber, Samuel Carton, Shi Feng, Q. Vera Liao, Tania Lombrozo, Alison Smith-Renner, Chenhao Tan

The NLP community are increasingly interested in providing explanations for NLP models to help people make sense of model behavior and potentially improve human interaction with models.

Evaluate What You Can't Evaluate: Unassessable Generated Responses Quality

no code implementations24 May 2023 Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang, Hinrich Schütze

There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.

Dialogue Generation

Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations

1 code implementation22 May 2023 Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He

We investigate the inductive biases of ICL from the perspective of feature bias: which feature ICL is more likely to use given a set of underspecified demonstrations in which two features are equally predictive of the labels.

Inductive Bias

Learning Human-Compatible Representations for Case-Based Decision Support

1 code implementation6 Mar 2023 Han Liu, Yizhou Tian, Chacha Chen, Shi Feng, Yuxin Chen, Chenhao Tan

Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans.

Classification Decision Making +1

Combinatorial Causal Bandits without Graph Skeleton

1 code implementation31 Jan 2023 Shi Feng, Nuoya Xiong, Wei Chen

This paper studies the CCB problem without the graph structure on binary general causal models and BGLMs.

Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts

1 code implementation12 Nov 2022 Xiaocui Yang, Shi Feng, Daling Wang, Pengfei Hong, Soujanya Poria

To improve the robustness of our model, we then leverage multiple diverse prompts for each input and propose a probabilistic method to fuse the output predictions.

Language Modelling Multimodal Sentiment Analysis

Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning

1 code implementation8 Nov 2022 Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang

Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs).

Contrastive Learning Knowledge Graphs

Active Example Selection for In-Context Learning

1 code implementation8 Nov 2022 Yiming Zhang, Shi Feng, Chenhao Tan

For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5. 8\%$ improvement on average.

DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection

no code implementations25 Oct 2022 Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.


MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation

1 code implementation COLING 2022 Yongkang Liu, Shi Feng, Daling Wang, Yifei Zhang

Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.

Data Augmentation Dialogue Generation

Combinatorial Causal Bandits

1 code implementation4 Jun 2022 Shi Feng, Wei Chen

For the special case of linear models with hidden variables, we apply causal inference techniques such as the do-calculus to convert the original model into a Markovian model, and then show that our BGLM-OFU algorithm and another algorithm based on the linear regression both solve such linear models with hidden variables.

Causal Inference

Machine Explanations and Human Understanding

1 code implementation8 Feb 2022 Chacha Chen, Shi Feng, Amit Sharma, Chenhao Tan

Our key result is that without assumptions about task-specific intuitions, explanations may potentially improve human understanding of model decision boundary, but they cannot improve human understanding of task decision boundary or model error.

Decision Making

Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks

1 code implementation ACL 2021 Xiaocui Yang, Shi Feng, Yifei Zhang, Daling Wang

In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection.

Calibrate Before Use: Improving Few-Shot Performance of Language Models

3 code implementations19 Feb 2021 Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, Sameer Singh

We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near state-of-the-art.

Few-Shot Learning

Concealed Data Poisoning Attacks on NLP Models

no code implementations NAACL 2021 Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input.

Data Poisoning Language Modelling +2

Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension

no code implementations WS 2019 Qian Li, Hui Su, Cheng Niu, Daling Wang, Zekang Li, Shi Feng, Yifei Zhang

Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.

Reading Comprehension reinforcement-learning +1

How Pre-trained Word Representations Capture Commonsense Physical Comparisons

no code implementations WS 2019 Pranav Goel, Shi Feng, Jordan Boyd-Graber

One type of common sense is how two objects compare on physical properties such as size and weight: e. g., {`}is a house bigger than a person?{'}.

Common Sense Reasoning

Universal Adversarial Triggers for Attacking and Analyzing NLP

1 code implementation IJCNLP 2019 Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, Sameer Singh

We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset.

Language Modelling Reading Comprehension

Misleading Failures of Partial-input Baselines

no code implementations ACL 2019 Shi Feng, Eric Wallace, Jordan Boyd-Graber

Recent work establishes dataset difficulty and removes annotation artifacts via partial-input baselines (e. g., hypothesis-only models for SNLI or question-only models for VQA).

Natural Language Inference Visual Question Answering (VQA)

Quizbowl: The Case for Incremental Question Answering

no code implementations9 Apr 2019 Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, Jordan Boyd-Graber

Throughout this paper, we show that collaborations with the vibrant trivia community have contributed to the quality of our dataset, spawned new research directions, and doubled as an exciting way to engage the public with research in machine learning and natural language processing.

BIG-bench Machine Learning Decision Making +1

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

1 code implementation1 Feb 2019 Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi

Second, we compute the importance of group-features in deep learning interpretation by introducing a sparsity regularization term.

Feature Importance General Classification

What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play

no code implementations23 Oct 2018 Shi Feng, Jordan Boyd-Graber

Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models.

BIG-bench Machine Learning Decision Making +1

Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning

no code implementations EMNLP 2018 Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem.

General Classification Multi-Task Learning +2

Pathologies of Neural Models Make Interpretations Difficult

no code implementations EMNLP 2018 Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan Boyd-Graber

In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word.

Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation

no code implementations COLING 2016 Shi Feng, Shujie Liu, Nan Yang, Mu Li, Ming Zhou, Kenny Q. Zhu

In neural machine translation, the attention mechanism facilitates the translation process by producing a soft alignment between the source sentence and the target sentence.

Machine Translation Translation

Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model

no code implementations13 Jan 2016 Shi Feng, Shujie Liu, Mu Li, Ming Zhou

Aiming to resolve these problems, we propose new variations of attention-based encoder-decoder and compare them with other models on machine translation.

Image Captioning Machine Translation +4

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