Search Results for author: Zhiheng Huang

Found 27 papers, 12 papers with code

Personalized Search Via Neural Contextual Semantic Relevance Ranking

no code implementations10 Sep 2023 Deguang Kong, Daniel Zhou, Zhiheng Huang, Steph Sigalas

Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference.

Document Ranking

Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks

1 code implementation19 Dec 2022 Kaiser Sun, Peng Qi, Yuhao Zhang, Lan Liu, William Yang Wang, Zhiheng Huang

We show that, with consistent tokenization, the model performs better in both in-domain and out-of-domain datasets, with a notable average of +1. 7 F2 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets.

Extractive Question-Answering Question Answering

Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations

no code implementations17 Dec 2022 Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Yang Wang, Zhiheng Huang

There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).

Multi-Task Learning

Attention-guided Generative Models for Extractive Question Answering

no code implementations12 Oct 2021 Peng Xu, Davis Liang, Zhiheng Huang, Bing Xiang

We propose a simple strategy to obtain an extractive answer span from the generative model by leveraging the decoder cross-attention patterns.

Extractive Question-Answering Open-Domain Question Answering +1

Multiplicative Position-aware Transformer Models for Language Understanding

no code implementations27 Sep 2021 Zhiheng Huang, Davis Liang, Peng Xu, Bing Xiang

Transformer models, which leverage architectural improvements like self-attention, perform remarkably well on Natural Language Processing (NLP) tasks.

Beyond [CLS] through Ranking by Generation

no code implementations EMNLP 2020 Cicero Nogueira dos santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in various IR tasks in the past.

Answer Selection Information Retrieval +4

Embedding-based Zero-shot Retrieval through Query Generation

1 code implementation22 Sep 2020 Davis Liang, Peng Xu, Siamak Shakeri, Cicero Nogueira dos Santos, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

In some cases, our model trained on synthetic data can even outperform the same model trained on real data

Passage Retrieval Retrieval

TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding

no code implementations16 Mar 2020 Zhiheng Huang, Peng Xu, Davis Liang, Ajay Mishra, Bing Xiang

Prior to the transformer era, bidirectional Long Short-Term Memory (BLSTM) has been the dominant modeling architecture for neural machine translation and question answering.

Machine Translation Natural Language Inference +4

WeNet: Weighted Networks for Recurrent Network Architecture Search

no code implementations8 Apr 2019 Zhiheng Huang, Bing Xiang

In this paper, we propose a novel way of architecture search by means of weighted networks (WeNet), which consist of a number of networks, with each assigned a weight.

General Classification Image Classification +2

Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition

1 code implementation22 Jan 2019 Julian Salazar, Katrin Kirchhoff, Zhiheng Huang

The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition.

Classification General Classification +3

Learning Noise-Invariant Representations for Robust Speech Recognition

no code implementations17 Jul 2018 Davis Liang, Zhiheng Huang, Zachary C. Lipton

Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs.

Data Augmentation Representation Learning +3

Residual Convolutional CTC Networks for Automatic Speech Recognition

no code implementations24 Feb 2017 Yisen Wang, Xuejiao Deng, Songbai Pu, Zhiheng Huang

Furthermore, we introduce a CTC-based system combination, which is different from the conventional frame-wise senone-based one.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

CNN-RNN: A Unified Framework for Multi-label Image Classification

1 code implementation CVPR 2016 Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.

Classification General Classification +2

Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks

no code implementations CVPR 2016 Haonan Yu, Jiang Wang, Zhiheng Huang, Yi Yang, Wei Xu

The sentence generator produces one simple short sentence that describes a specific short video interval.

Video Captioning

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

1 code implementation ICCV 2015 Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan Yuille

In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.

Image Captioning Novel Concepts

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

2 code implementations20 Dec 2014 Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan Yuille

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.

Image Captioning Retrieval

ImageNet Large Scale Visual Recognition Challenge

12 code implementations1 Sep 2014 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.

General Classification Image Classification +3

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