Papers with pre-training task
CharBERT: Character-aware Pre-trained Language Model (2020.coling-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations . but these methods split a word into subword units and make it incomplete and fragile . |
| Approach: | They propose a character-aware pre-trained language model to tackle OOV problems . they construct contextual word embedding for each token from sequential character representations . |
| Outcome: | The proposed model improves on the existing models on multiple NLP benchmarks. |
Pre-Training Methods for Question Reranking (2024.eacl-short)
Copied to clipboard
| Challenge: | Existing methods for Question Answering to search for semantically similar questions are not suitable for new questions. |
| Approach: | They propose an unsupervised method for retrieving and ranking questions . they use a question retrieval model and a selection model to rerank questions based on their relevance . |
| Outcome: | The proposed method achieves state-of-the-art performance on QRC and Quora-match datasets . it provides better and cheaper access to answers than the system generated them . |
Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing non-AutoRegressive (NAR) text generation models lack proper pre-training, making them far behind pre-trained autoregressive models. |
| Approach: | They propose a novel pre-training task to promote prediction consistency in non-autoregressive (NAR) generation. |
| Outcome: | The proposed model outperforms existing pre-trained models and achieves 17 times speedup in throughput. |
Weakly Supervised Pre-Training for Multi-Hop Retriever (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for weakly supervised multi-hop pretraining require costly human annotation. |
| Approach: | They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training. |
| Outcome: | The proposed method is effective and robust on limited data and computational resources. |
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal Products (2022.coling-1)
Copied to clipboard
| Challenge: | Existing pre-trained language models lack medicinal product knowledge for product vertical search. |
| Approach: | They propose a biomedical knowledge enhanced pre-trained language model for medicinal product vertical search using ELECTRA’s replaced token detection (RTD) pre-training. |
| Outcome: | The proposed model improves query-title relevance, query intent classification, and named entity recognition in query. |
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | a number of information extraction tasks require task-specific training. |
| Approach: | They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model . |
| Outcome: | The proposed framework outperforms the existing methods on open information extraction tasks. |
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
| Approach: | They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval. |
| Outcome: | The proposed system outperforms closed-book QA and can answer multi-hop questions. |
Structural Contrastive Pretraining for Cross-Lingual Comprehension (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to train multilingual language models using pretraining tasks like mask language modeling have yielded promising results on a wide range of downstream tasks. |
| Approach: | They propose a new task to align the structural words in a parallel sentence, enhancing models’ ability to comprehend cross-lingual representations. |
| Outcome: | The proposed task improves model's ability to comprehend cross-lingual representations by increasing the frequency of negative pairings. |
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to train cross-lingual pre-trained language models have shown great success in cross-linguistic sequence labeling tasks. |
| Approach: | They propose a cross-lingual language informative span masking task to eliminate the objective gap between pre-training and fine-tuning stages. |
| Outcome: | The proposed method surpasses the state-of-the-art methods on multiple benchmarks even with limited pre-training data. |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
Copied to clipboard
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
| Challenge: | Large pre-trained models have improved performance on a variety of natural language processing tasks. |
| Approach: | They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators. |
| Outcome: | The proposed model performs better on two NL-PL applications by fine-tuning model parameters. |
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | Transfer learning is a promising direction for low-resource neural machine translation (NMT) but it introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. |
| Approach: | They conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. |
| Outcome: | The results suggest that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural machine translation. |
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models can be fine-tuned on domain-specific unlabeled data . however, most further pre-training works just keep running the conventional pre- training task . |
| Approach: | They propose to add a further pre-training phase to the model to improve downstream tasks . they propose to use a domain-adaptive pre-tuning phase to fine-tune the models on unlabeled data . |
| Outcome: | The proposed method improves multiple task-oriented dialogue downstream tasks. |
Symbolization, Prompt, and Classification: A Framework for Implicit Speaker Identification in Novels (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for speaker identification in novel dialogues are limited to handling explicit narrative patterns and complex cases. |
| Approach: | They propose a framework which identifies implicit speakers in novels via symbolization, prompt, and classification. |
| Outcome: | The proposed framework outperforms existing methods by 4.8% accuracy on the web novel collection, which reduces 47% of speaker identification errors, and outperfies the emerging ChatGPT. |
Span Selection Pre-training for Question Answering (2020.acl-main)
Copied to clipboard
Michael Glass, Alfio Gliozzo, Rishav Chakravarti, Anthony Ferritto, Lin Pan, G P Shrivatsa Bhargav, Dinesh Garg, Avi Sil
| Challenge: | Pre-trained BERTs provide large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). |
| Approach: | They propose a new pre-training task inspired by reading comprehension to better align the pre- training from memorization to understanding. |
| Outcome: | The proposed model outperforms BERT-BASE and BERT LARGE on a new dataset and improves answer prediction F1 by 4 points and supporting fact prediction F1. |
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)
Copied to clipboard
Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou
| Challenge: | Existing methods for learning cross-lingual representations are lacking in the field of NLP. |
| Approach: | They propose a framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. |
| Outcome: | The proposed approach improves cross-lingual transferability on benchmarks. |
SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion (2020.lrec-1)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained models can learn unsupervised language representations by self-supervised tasks on large-scale corpora. |
| Approach: | They propose a pre-training task called Sentence Insertion for Chinese query-passage pairs NLP tasks . they propose 'word segmentation' method to enhance Chinese Bert performance . |
| Outcome: | The proposed task improves Chinese pre-trained models significantly. |
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Cross-domain sentiment classification requires large amounts of labeled data. |
| Approach: | They propose to apply a pre-training language model BERT on unsupervised domain adaptation . they propose to distill domain-specific features in a self-supervised way . |
| Outcome: | The proposed model outperforms state-of-the-art methods on Amazon dataset . it can be applied to the unsupervised domain adaptation task without domain awareness . |
Syntax-Enhanced Pre-trained Model (2021.acl-long)
Copied to clipboard
Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan
| Challenge: | Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages. |
| Approach: | They propose a model that utilizes the syntactic structure of text in pre-training and fine-tuning stages. |
| Outcome: | The proposed model achieves state-of-the-art on six public benchmark datasets. |
Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment (2023.acl-long)
Copied to clipboard
Tianshu Yu, Haoyu Gao, Ting-En Lin, Min Yang, Yuchuan Wu, Wentao Ma, Chao Wang, Fei Huang, Yongbin Li
| Challenge: | Existing speech-text pre-training methods are limited to one or two specific tasks, despite their success in speech-language processing tasks. |
| Approach: | They propose a temporal position prediction task to capture the speech-text alignment . they use a textual dialog pre-training task to generalize a response selection task . |
| Outcome: | The proposed model is superior in learning speech-text alignment and multi-turn dialog context. |
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing pre-trained models neglect to consider linguistic knowledge of texts . existing models neglect linguistic information, which is important for sentiment analysis . |
| Approach: | They propose a model that introduces word-level linguistic knowledge into pre-trained models to enhance sentiment analysis by querying SentiWordNet to acquire sentiment polarity. |
| Outcome: | The proposed model obtains state-of-the-art performance on a variety of sentiment analysis tasks. |
End-to-End Unsupervised Vision-and-Language Pre-training with Referring Expression Matching (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing unsupervised vision-and-language pre-training methods take pre-extracted region-based visual features from external object detectors, which limits flexibility and reduces computational efficiency. |
| Approach: | They propose an unsupervised vision-and-language pre-training task that predicts which patches contain an object referred to in natural language from the encoded visual features. |
| Outcome: | The proposed approach outperforms existing methods and obtains state-of-the-art results on four vision-and-language tasks. |
KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)
Copied to clipboard
Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen
| Challenge: | Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings. |
| Approach: | They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. |
| Outcome: | The proposed framework can be used in few-shot settings and reduces data requirements. |
Efficient Data Learning for Open Information Extraction with Pre-trained Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results indicate that, compared to previous SOTA methods, OK-IE requires only 1/100 of the training data (900 instances) and 1/120 of the time (3 minutes) to achieve comparable results. |
| Approach: | They propose a framework that transforms OpenIE into the pre-training task form of the T5 model, thereby reducing the need for extensive training data. |
| Outcome: | The proposed framework transforms OpenIE into the pre-training task form of the T5 model, reducing the need for extensive training data and significantly reducing training time. |
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation (2025.findings-acl)
Copied to clipboard
| Challenge: | Traditional video topic segmentation methods struggle to discern topical transitions . supervised approaches have improved performance on video action or scene segmentation . |
| Approach: | They propose a new task for video topic segmentation that enhances multimodality alignment and fusion by exploring different architectures using Cross-Attention and Mixture of Experts. |
| Outcome: | The proposed model improves on educational videos, in the form of lectures . it combines cross-attention and mixture of experts to strengthen multimodality alignment and fusion . |
Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to persona simulation large language models (LLMs) focus on learning basic biographical information, or using limited role-play dialogue datasets to capture a character’s responses. |
| Approach: | They propose to train characters using a linguistic structure and a style-tuning mechanism that allows a general linguistic style expert to collaborate with other task-specific experts to better understand their thoughts. |
| Outcome: | The proposed model outperforms baselines on linguistic accuracy and opinion comprehension on three tasks for Lu Xun's essay collection. |