Papers by Zhongqiang Huang
Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)
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| Challenge: | Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains. |
| Approach: | They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on 21 datasets. |
Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies . |
| Approach: | They propose a more flexible approach by decoupling the adaptive policy model from the translation model. |
| Outcome: | The proposed approach outperforms baseline approaches in translation tasks. |
wav2vec-S: Adapting Pre-trained Speech Models for Streaming (2024.findings-acl)
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| Challenge: | Pre-trained speech models have advanced speech-related tasks, including speech recognition and translation. |
| Approach: | They propose a pre-trained speech model that incorporates modifications to ensure consistent speech representations during training and inference phases for streaming speech inputs. |
| Outcome: | The proposed model outperforms baseline models on speech recognition and translation tasks and achieves a superior balance between quality and latency. |
Word Reordering for Zero-shot Cross-lingual Structured Prediction (2021.emnlp-main)
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| Challenge: | Current sentence encoders are word order sensitive, resulting in poor performance . Adapting word order from one language to another is key in cross-lingual structured prediction. |
| Approach: | They propose a new module to organize words following the source language order . they build structured prediction models with bag-of-words inputs and introduce a module to do this . |
| Outcome: | The proposed model significantly improves target language performance for languages that are distant from the source language. |
Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)
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| Challenge: | Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed. |
| Approach: | They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation. |
| Outcome: | The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set. |
Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. |
| Approach: | They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations. |
| Outcome: | The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints. |
Towards Zero-shot Learning for End-to-end Cross-modal Translation Models (2023.findings-emnlp)
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| Challenge: | End-to-end zero-shot speech translation model is based on a zero-shot approach, but it is less competitive because of the limited amount of data available for multiple modalities. |
| Approach: | They propose an end-to-end zero-shot speech translation model that connects two pre-trained uni-modality modules via word rotator’s distance. |
| Outcome: | The proposed model performs better than or as well as those of the CTC-based models and can be trained in an end-to-end style to avoid error propagation. |
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023.findings-acl)
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| Challenge: | Existing augmentation techniques manipulate words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the text. |
| Approach: | They propose a novel Entity-to-Text based data augmentation technique called EnTDA to add, delete, replace or swap entities in the original text. |
| Outcome: | The proposed technique generates semantically coherent and entity preserving texts on thirteen NER tasks and two settings. |
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021.acl-long)
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| Challenge: | Recent work shows document-level contexts can significantly improve Named Entity Recognition models. |
| Approach: | They propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine with the original sentence as the query. |
| Outcome: | The proposed approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains. |
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)
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| Challenge: | Song translation requires both translation of lyrics and alignment of music notes . human translators of songs need to have a mastery of cultural traditions and the poetic usage of both source and target languages . |
| Approach: | They propose a model that can model lyric translation and lyrics-melody alignment . they use an encoder-decoder framework that can translate lyrics and determine number of aligned notes . |
| Outcome: | The proposed framework can translate lyrics and determine the number of aligned notes at each decoding step. |
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval (D19-61)
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| Challenge: | Low resource languages often lack relevance annotations for cross-lingual information retrieval . when available, the training data has limited coverage for possible queries . |
| Approach: | They propose a weakly supervised neural model for Cross-lingual information retrieval from low-resource languages using weak supervision instead of relevance annotations. |
| Outcome: | The proposed model achieves 19 MAP points improvement compared to CNNs and 12 points improvement from machine translation-based CLIR models. |
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor (2021.acl-long)
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Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
| Challenge: | Knowledge distillation is a technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). |
| Approach: | They propose a factorized form of the knowledge distillation objective for structured prediction which is tractable for many typical choices of the teacher and student models. |
| Outcome: | The proposed model is able to transfer knowledge between teacher and student models without loss of accuracy under four different scenarios. |
Improve Speech Translation Through Text Rewrite (2025.coling-industry)
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| Challenge: | Recent advances in speech translation (ST) research have focused on the unique characteristics of spontaneous speech, including accents and presentation quality. |
| Approach: | They propose to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text. |
| Outcome: | Experiments on public and in-house translation models show that the proposed model can be effectively distilled into a standalone translation model. |
Automated Concatenation of Embeddings for Structured Prediction (2021.acl-long)
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| Challenge: | Recent work shows that better word representations can be obtained by concatenating different types of embeddings. |
| Approach: | They propose to automate the process of finding better concatenated embeddings for structured prediction tasks by concatending different types of embeddables. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art with fine-tuned embeddings on 6 tasks and 21 datasets. |
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision (2021.acl-long)
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| Challenge: | Existing work on cross-lingual transfer learning focuses on transferring knowledge from high-resource languages to low-resourced ones. |
| Approach: | They propose a multi-view framework that integrates multiple source models into an aggregated source view and transfers it to a target view based on a task-specific model. |
| Outcome: | The proposed framework improves on three structured prediction tasks on 16 datasets. |
Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference (2023.emnlp-main)
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| Challenge: | Existing approaches to streaming speech translation use an offline model with a wait-k policy . however, there is a mismatch problem with an offline inference model trained with complete utterances . |
| Approach: | They propose an offline streaming speech translation model with wait-k policy to support different latency requirements. |
| Outcome: | The proposed model achieves better trade-offs between translation quality and latency than baselines. |
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)
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| Challenge: | Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data . |
| Approach: | They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space. |
| Outcome: | The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines. |
BLSP-Emo: Towards Empathetic Large Speech-Language Models (2024.emnlp-main)
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| Challenge: | BLSP-Emo model understands both semantics and emotions in speech and generates empathetic responses. |
| Approach: | They propose a language-speech pretraining with emotion support that utilizes existing speech and emotion recognition datasets to create an end-to-end speech-language model. |
| Outcome: | The proposed model can understand both semantics and emotions in speech and generate empathetic responses. |
A Unified Encoding of Structures in Transition Systems (2021.emnlp-main)
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| Challenge: | Existing approaches to encode dynamic structures only encode partial information of structures. |
| Approach: | They propose a new attention-based encoder unifying all structures in a transition system. |
| Outcome: | The proposed method significantly improves the test speed and achieves the best transition-based model. |
Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention (2024.lrec-main)
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| Challenge: | Existing methods based on deep learning struggle to grasp idiom semantics due to the figurative meanings of many idiomas deviating from their literal interpretations. |
| Approach: | They propose a Chinese idiom cloze test to capture comprehensive idiomatics and a semantic sense contrastive learning module to enhance the representation of idiomics. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Chinese idiom cloze test and on other benchmark datasets. |
Training Simultaneous Speech Translation with Robust and Random Wait-k-Tokens Strategy (2023.emnlp-main)
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| Challenge: | Simultaneous Speech Translation (SimulST) is a task focused on ensuring high-quality translation of speech in low-latency situations. |
| Approach: | They propose a token-level cross-modal alignment method to improve the translation of text to audio . they use audio transcription pairs to pre-train the encoder and a random wait-k-tokens strategy to optimize the task. |
| Outcome: | The proposed method achieves better trade-off between translation quality and latency. |
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)
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| Challenge: | Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations. |
| Approach: | They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts . |
| Outcome: | The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information. |
Manifold Adversarial Augmentation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Recent studies show that NMT models can drop significantly when small perturbations are added to input sentences. |
| Approach: | They propose a data augmentation approach to sample sentences from the vicinity distributions in higher-level representations. |
| Outcome: | The proposed method improves translation accuracy on training samples from higher-level representations. |
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)
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| Challenge: | Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions. |
| Approach: | They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. |
| Outcome: | The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets. |
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |
An Investigation of Potential Function Designs for Neural CRF (2020.findings-emnlp)
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| Challenge: | Existing approaches to sequence labeling are based on the neural linear-chain CRF model. |
| Approach: | They propose a series of increasingly expressive potential functions for neural CRF models that integrate emission and transition functions and explicitly take contextual words as input. |
| Outcome: | The proposed model consistently achieves the best performance on the decomposed quadrilinear potential function based on the representations of two neighboring labels and two neighbored words. |
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network (2020.emnlp-main)
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| Challenge: | Existing approaches to sequence labeling require sequential computation that makes parallelization impossible. |
| Approach: | They propose to employ a parallelizable approximate variational inference algorithm for the CRF model. |
| Outcome: | The proposed approach improves decoding speed and accuracy with long sentences and is parallelizable for faster training and prediction. |