652 citations · 1.7k across the 25 of their papers we have counts for
26 papers
Weyl phonons in chiral crystals
Tiantian Zhang, Zhiheng Huang, Zitian Pan +3
Chirality is an indispensable concept that pervades fundamental science and nature, manifesting itself in diverse forms such as chiral quasiparticles and chiral structures. Of part…
Electron-infrared phonon coupling in ABC trilayer graphene
Xiaozhou Zan, Xiangdong Guo, Aolin Deng +18
Stacking order plays a crucial role in determining the crystal symmetry and has significant impacts on electronic, optical, magnetic, and topological properties. Electron-phonon co…
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
Danilo Ribeiro, Shen Wang, Xiaofei Ma +10
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect mo…
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
Jifan Chen, Yuhao Zhang, Lan Liu +5
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). However, existing met…
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks
Kaiser Sun, Peng Qi, Yuhao Zhang +3
Generative models have been widely applied to solve extractive tasks, where parts of the input is extracted to form the desired output, and achieved significant success. For exampl…
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Xiyang Hu, Xinchi Chen, Peng Qi +4
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR…
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Danilo Ribeiro, Shen Wang, Xiaofei Ma +8
Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations,…
Contrastive Document Representation Learning with Graph Attention Networks
Peng Xu, Xinchi Chen, Xiaofei Ma +2
Recent progress in pretrained Transformer-based language models has shown great success in learning contextual representation of text. However, due to the quadratic self-attention…
Attention-guided Generative Models for Extractive Question Answering
Peng Xu, Davis Liang, Zhiheng Huang +1
We propose a novel method for applying Transformer models to extractive question answering (QA) tasks. Recently, pretrained generative sequence-to-sequence (seq2seq) models have ac…
Multiplicative Position-aware Transformer Models for Language Understanding
Zhiheng Huang, Davis Liang, Peng Xu +1
Transformer models, which leverage architectural improvements like self-attention, perform remarkably well on Natural Language Processing (NLP) tasks. The self-attention mechanism…
Spatially indirect intervalley excitons in bilayer WSe2
Zhiheng Huang, Yanchong Zhao, Tao Bo +16
Spatially indirect excitons with displaced wavefunctions of electrons and holes play a pivotal role in a large portfolio of fascinating physical phenomena and emerging optoelectron…
Beyond [CLS] through Ranking by Generation
Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati +2
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 variou…
Improve Transformer Models with Better Relative Position Embeddings
Zhiheng Huang, Davis Liang, Peng Xu +1
Transformer architectures rely on explicit position encodings in order to preserve a notion of word order. In this paper, we argue that existing work does not fully utilize positio…
Embedding-based Zero-shot Retrieval through Query Generation
Davis Liang, Peng Xu, Siamak Shakeri +4
Passage retrieval addresses the problem of locating relevant passages, usually from a large corpus, given a query. In practice, lexical term-matching algorithms like BM25 are popul…
TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding
Zhiheng Huang, Peng Xu, Davis Liang +2
Bidirectional Encoder Representations from Transformers (BERT) has recently achieved state-of-the-art performance on a broad range of NLP tasks including sentence classification, m…
WeNet: Weighted Networks for Recurrent Network Architecture Search
Zhiheng Huang, Bing Xiang
In recent years, there has been increasing demand for automatic architecture search in deep learning. Numerous approaches have been proposed and led to state-of-the-art results in…
Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition
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. Separately, connectionist temporal classific…
Learning Noise-Invariant Representations for Robust Speech Recognition
Davis Liang, Zhiheng Huang, Zachary C. Lipton
Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an o…
Residual Convolutional CTC Networks for Automatic Speech Recognition
Yisen Wang, Xuejiao Deng, Songbai Pu +1
Deep learning approaches have been widely used in Automatic Speech Recognition (ASR) and they have achieved a significant accuracy improvement. Especially, Convolutional Neural Net…
CNN-RNN: A Unified Framework for Multi-label Image Classification
Jiang Wang, Yi Yang, Junhua Mao +3
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 mu…
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
Haonan Yu, Jiang Wang, Zhiheng Huang +2
We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a…
Bidirectional LSTM-CRF Models for Sequence Tagging
Zhiheng Huang, Wei Xu, Kai Yu
In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, L…
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
Haoyuan Gao, Junhua Mao, Jie Zhou +3
In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contain…
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
Junhua Mao, Wei Xu, Yi Yang +3
In this paper, we address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions. Using linguistic con…