255 citations · 424 across the 21 of their papers we have counts for
22 papers
LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search
Liang Huang, He Zhang, Dezhong Deng +4
Motivation: Predicting the secondary structure of an RNA sequence is useful in many applications. Existing algorithms (based on dynamic programming) suffer from a major limitation:…
Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report
Renjie Zheng, Hairong Liu, Mingbo Ma +2
This paper describes the machine translation system developed jointly by Baidu Research and Oregon State University for WMT 2019 Machine Translation Robustness Shared Task. Transla…
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning
Baigong Zheng, Renjie Zheng, Mingbo Ma +1
Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged mo…
Learning to Stop in Structured Prediction for Neural Machine Translation
Mingbo Ma, Renjie Zheng, Liang Huang
Beam search optimization resolves many issues in neural machine translation. However, this method lacks principled stopping criteria and does not learn how to stop during training,…
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework
Mingbo Ma, Liang Huang, Hao Xiong +9
Simultaneous translation, which translates sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences. While the con…
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
Hairong Liu, Mingbo Ma, Liang Huang +2
Neural machine translation (NMT) is notoriously sensitive to noises, but noises are almost inevitable in practice. One special kind of noise is the homophone noise, where words are…
Speeding Up Neural Machine Translation Decoding by Cube Pruning
Wen Zhang, Liang Huang, Yang Feng +2
Although neural machine translation has achieved promising results, it suffers from slow translation speed. The direct consequence is that a trade-off has to be made between transl…
When to Finish? Optimal Beam Search for Neural Text Generation (modulo beam size)
Liang Huang, Kai Zhao, Mingbo Ma
In neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality. However, in the ne…
Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report
Renjie Zheng, Yilin Yang, Mingbo Ma +1
This paper describes multimodal machine translation systems developed jointly by Oregon State University and Baidu Research for WMT 2018 Shared Task on multimodal translation. In t…
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation
Renjie Zheng, Mingbo Ma, Liang Huang
Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently. However, during training time, typically only…
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
Yilin Yang, Liang Huang, Mingbo Ma
Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search. It has been widely observed that, however, beam sizes…
Linear-Time Constituency Parsing with RNNs and Dynamic Programming
Juneki Hong, Liang Huang
Recently, span-based constituency parsing has achieved competitive accuracies with extremely simple models by using bidirectional RNNs to model "spans". However, the minimal span p…
Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Reza Ghaeini, Xiaoli Z. Fern, Liang Huang +1
Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But…
OSU Multimodal Machine Translation System Report
Mingbo Ma, Dapeng Li, Kai Zhao +1
This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I". In this task, all the sentence pairs are image captions in dif…
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks
Mingbo Ma, Kai Zhao, Liang Huang +2
Sentence-level classification and sequential labeling are two fundamental tasks in language understanding. While these two tasks are usually modeled separately, in reality, they ar…
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
Tianze Shi, Liang Huang, Lillian Lee
We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of…
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank
Kai Zhao, Liang Huang
Discourse parsing has long been treated as a stand-alone problem independent from constituency or dependency parsing. Most attempts at this problem are pipelined rather than end-to…
Textual Entailment with Structured Attentions and Composition
Kai Zhao, Liang Huang, Mingbo Ma
Deep learning techniques are increasingly popular in the textual entailment task, overcoming the fragility of traditional discrete models with hard alignments and logics. In partic…
Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles
James Cross, Liang Huang
Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks. Despite striking results in dependency parsing, howe…
Incremental Parsing with Minimal Features Using Bi-Directional LSTM
James Cross, Liang Huang
Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created…
Dependency-based Convolutional Neural Networks for Sentence Embedding
Mingbo Ma, Liang Huang, Bing Xiang +1
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequenti…
Type-Driven Incremental Semantic Parsing with Polymorphism
Kai Zhao, Liang Huang
Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new tech…