Papers with shared encoder
Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
| Outcome: | The proposed approach achieves significant improvements on English-German, English-French and Chinese-to-English translation tasks. |
Multi-Task Neural Model for Agglutinative Language Translation (2020.acl-srw)
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| Challenge: | Neural machine translation (NMT) has been gaining popularity in high-resource translation tasks, but struggles in low-ressource and morphologically-rich scenarios. |
| Approach: | They propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming. |
| Outcome: | The proposed model can significantly improve translation performance on agglutinative languages by using a small amount of monolingual data. |
Discourse Parsing Enhanced by Discourse Dependence Perception (2022.aacl-main)
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| Challenge: | Top-down neural models still suffer from the top-down error propagation issue . previous studies gradually switch from feature-based machine learning methods to deep neural models . |
| Approach: | They propose a top-down framework that learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders. |
| Outcome: | The proposed framework learns from discourse dependency and constituency parsing through one shared encoder and two independent decoders on a Chinese discourse corpus. |
Sharing Encoder Representations across Languages, Domains and Tasks in Large-Scale Spoken Language Understanding (2023.acl-industry)
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Jonathan Hueser, Judith Gaspers, Thomas Gueudre, Chandana Prakash, Jin Cao, Daniil Sorokin, Quynh Do, Nicolas Anastassacos, Tobias Falke, Turan Gojayev
| Challenge: | Larger encoders can improve accuracy for spoken language understanding (SLU) but are difficult to use given the inference latency constraints of online systems. |
| Approach: | They propose to use a larger 170M parameter BERT encoder that shares representations across languages, domains and tasks for SLU. |
| Outcome: | The proposed encoders achieve state-of-the-art performance on numerous NLP tasks. |
Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)
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| Challenge: | Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks. |
| Approach: | They propose a multi-task neural model to jointly extract entities and negations from medical reports. |
| Outcome: | The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset. |
Unsupervised Neural Text Simplification (P19-1)
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| Challenge: | Existing unsupervised methods for text simplification are limited to unlabeled text . paper aims to improve the performance of unsupervised systems by incorporating labeled pairs . |
| Approach: | They propose to use unlabeled text to train a neural text simplification framework . they propose to add a pair of attentional-decoders to the framework to improve performance . |
| Outcome: | The proposed model outperforms existing supervised methods on public test data. |
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict? (2022.findings-emnlp)
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| Challenge: | Existing multi-task learning architectures learn a single model across multiple tasks through a shared encoder followed by task-specific decoders. |
| Approach: | They propose to use a shared encoder and language model decoder to learn a single model across multiple tasks. |
| Outcome: | The proposed architecture does surprisingly well across a range of diverse tasks. |
Efficient Large-Scale Neural Domain Classification with Personalized Attention (P18-1)
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| Challenge: | Using a scalable neural model, we show that personalization improves domain classification accuracy in a setting with thousands of overlapping domains. |
| Approach: | They propose a scalable neural model architecture with a shared encoder that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. |
| Outcome: | The proposed architecture achieves two orders of magnitude faster than full model retraining. |
Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (2024.lrec-main)
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| Challenge: | Existing methods for automating medical coding focus on a single coding system . however, there are still challenges to overcome in coding. |
| Approach: | They propose a joint learning framework for Across Medical coding systems which jointly learns different coding system through multi-task learning. |
| Outcome: | The proposed framework improves the performance of the MIMIC-IV ICD-9 and MIMICIV I CD-10 datasets. |
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)
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| Challenge: | Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. |
| Approach: | They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy . |
| Outcome: | The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks. |
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals (2021.acl-long)
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| Challenge: | Existing studies integrate word embeddings with cognitive features into neural models of natural language processing (NLP) but there are some issues in the use of cognitive features in NLP. |
| Approach: | They propose a cog-align approach that aligns textual and cognitive inputs to capture differences and commonalities. |
| Outcome: | The proposed model improves on three NLP tasks with multiple cognitive features over state-of-the-art models. |
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders (P19-1)
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| Challenge: | Existing approaches to train multiple languages with a shared encoder and multiple decoders are based on denoising autoencoding of each language and back-translating between English and multiple non-English languages. |
| Approach: | They propose a multilingual unsupervised NMT scheme which trains multiple languages with a shared encoder and multiple decoders. |
| Outcome: | The proposed model performs better than the separately trained bilingual models on monolingual corpora and improves by 1.48 BLEU points on WMT test sets. |
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
| Approach: | They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets. |
Improving Domain Adaptation Translation with Domain Invariant and Specific Information (N19-1)
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| Challenge: | Neural machine translation models are based on the encoder-decoder architecture, which makes them overfitting to frequent observations. |
| Approach: | They propose a method to explicitly model out-of-domain information in an encoder-decoder framework . they propose combining out- of-domain training data with out-out-of domain data . |
| Outcome: | The proposed method outperforms baselines on multiple data sets. |
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning (D19-1)
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| Challenge: | Existing automated generation of articles' characteristics is inconsistent if they are generated individually. |
| Approach: | They propose a multi-task learning model with a shared encoder and multiple decoders for each task. |
| Outcome: | The proposed model generates more consistent headlines, key phrases and categories . it outperforms baseline model on the ROUGE scores and generates fluent headlines . |
Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging (N19-1)
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| Challenge: | Low-resource language name tagging is an important but challenging task. |
| Approach: | They propose a neural architecture that leverages multi-level adversarial transfer to improve name tagging for low-resource languages. |
| Outcome: | The proposed approach outperforms previous approaches on CoNLL data sets. |
Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR (2021.findings-acl)
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| Challenge: | Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency. |
| Approach: | They propose a new paradigm for simultaneous speech-to-text translation using two separate decoders . they use multitask learning to jointly learn these two tasks with a shared encoder . |
| Outcome: | The proposed method achieves substantially better translation quality at similar levels of latency. |
Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities (D19-1)
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| Challenge: | Named entity recognition (NER) aims at identifying shallow semantic elements in text. |
| Approach: | They propose a neural two-stage approach to recognizing discontiguous and overlapping entities by decomposing the problem into two subtasks. |
| Outcome: | The proposed model achieves state-of-the-art in a standard dataset even without external features. |
High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
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| Challenge: | Syntactic parsing aims to reveal how sentences are syntactically structured. |
| Approach: | They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase . |
| Outcome: | The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm . |
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue (2023.acl-long)
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| Challenge: | Evaluating open-domain dialogue systems is challenging because of the one-to-many problem. |
| Approach: | They propose a reference-based dialogue evaluation approach that leverages the pre-created utterance as reference other than the gold response to relieve the one-to-many problem. |
| Outcome: | The proposed method outperforms state-of-the-art evaluation methods on three datasets and two existing benchmarks. |
PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (2023.acl-long)
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| Challenge: | Image translation is a task that translates an image containing text in the source language to the target language. |
| Approach: | They propose an end-to-end image translation framework that bridges the modality gap between visual inputs and textual inputs/outputs of machine translation (MT). |
| Outcome: | The proposed framework outperforms existing models on a large-scale image translation corpus . it significantly outperformed both cascaded and strong models on the e-commerce domain . |
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis (2021.emnlp-main)
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Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, Xiaozhong Liu
| Challenge: | Recent efforts to predict chatbot failure hatches vital apprehensions due to complexity of human conversation. |
| Approach: | They propose a model that integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. |
| Outcome: | The proposed model integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. |
Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline (2026.acl-long)
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| Challenge: | Existing facial forgery detection methods focus on binary classification or pixel-level localization, providing little semantic insight into the nature of the manipulation. |
| Approach: | They propose a multimodal task that localizes forged regions and generates natural language explanations grounded in editing process. |
| Outcome: | The proposed task localizes forged regions and generates natural language explanations grounded in editing process. |