Papers with NLP downstream tasks
T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (2021.eacl-demos)
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| Challenge: | Language model (LM) pretraining has led to consistent improvements in many downstream tasks, including named entity recognition (NER). |
| Approach: | They propose a Python library for NER LM finetuning that facilitates cross-domain and cross-lingual generalization of LMs finetuned on NER. |
| Outcome: | The proposed library outperforms LMs trained on NERs in cross-domain and cross-lingual generalization tests on nine datasets. |
Retrieval-augmented Generation across Heterogeneous Knowledge (2022.naacl-srw)
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| Challenge: | Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19. |
| Approach: | They propose to use a single-source homogeneous corpus to generate retrieval-augmented generation models that can learn from the pre-training corpus. |
| Outcome: | The proposed methods have been applied to various knowledge-intensive NLP tasks, but most of the work has focused on retrieving unstructured text documents from Wikipedia. |
A Thorough Evaluation of Task-Specific Pretraining for Summarization (2021.emnlp-main)
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| Challenge: | Previous work has used task-agnostic pretraining methods like masked language models or corrupted span prediction to improve performance on downstream tasks. |
| Approach: | They propose to use a task-agnostic pretraining to improve on low-resource tasks. |
| Outcome: | The proposed model can predict extracted gap sentences on summarization with a low resource and zero shot setup. |
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
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| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
LINSPECTOR WEB: A Multilingual Probing Suite for Word Representations (D19-3)
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| Challenge: | LINSPECTOR WEB is an open source multilingual inspector to analyze word embeddings. |
| Approach: | They propose to use LINSPECTOR WEB to analyze word embeddings in 28 languages. |
| Outcome: | The system performs 16 simple linguistic probing tasks for a diverse set of 28 languages. |
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing (2021.acl-long)
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| Challenge: | Existing studies restrict modal expressions to a closed syntactic class . modal sense labels are vastly different across different studies, lacking an accepted standard . |
| Approach: | They propose a task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. |
| Outcome: | The proposed task is based on the Georgetown Gradable Modal Expressions corpus . it detects and classifies fine-grained modal concepts and associates them with modified events . |
Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings (N19-1)
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| Challenge: | Existing distributional semantic models cannot capture the distinct meanings of polysemous words, resulting in conflated word representations of diverse contextual semantics. |
| Approach: | They propose a distributional semantic model that learns multiple representations of a word based on different topics. |
| Outcome: | The proposed model outperforms single-prototype models on NLP downstream tasks. |
Dict-BERT: Enhancing Language Model Pre-training with Dictionary (2022.findings-acl)
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Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang
| Challenge: | Pre-trained language models (PLMs) capture word semantics in different contexts, hence the embeddings of rare words on the tail are poorly optimized. |
| Approach: | They propose to leverage definitions of rare words in dictionaries to enhance language model pre-training by leveraging dictionary definitions. |
| Outcome: | The proposed model improves understanding of rare words and boosts performance on various NLP downstream tasks. |
Hierarchical Pretraining on Multimodal Electronic Health Records (2023.emnlp-main)
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| Challenge: | Existing pretraining models on EHR data are too specific, limiting their transferability. |
| Approach: | They propose a general, unified pretraining framework for hierarchically multimodal EHR data that can be used to train models on a large dataset before fine-tuning it on 'upstream' tasks. |
| Outcome: | The proposed model performs on eight downstream tasks spanning three levels and compares with baselines on 18 different tasks. |
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games (2021.eacl-main)
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Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Using guessing games, an artificial agent can learn to perform on novel downstream tasks such as Visual Question Answering (VQA). |
| Approach: | They propose a supervised learning scenario in which an agent learns to mimic successful guessing games and a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning. |
| Outcome: | The proposed model can be applied to a VQA dataset using a supervised learning scenario and a novel way for an agent to play by itself. |
Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation (2020.findings-emnlp)
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| Challenge: | Recent research shows word embeddings have strong gender biases in embeddable spaces . a proposed method can be used to debiase word embeds without loss of semantic information . |
| Approach: | They propose a latent disentanglement method with a siamese auto-encoder structure with an adapted gradient reversal layer to debiase word embeddings. |
| Outcome: | The proposed method can preserve semantic information during debiasing while minimizing loss of semantic information for extrinsic NLP tasks. |
AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)
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| Challenge: | Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models . |
| Approach: | They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models. |
| Outcome: | The proposed method outperforms standard prompt-based methods in few-shot settings. |
Frequency Balanced Datasets Lead to Better Language Models (2023.findings-emnlp)
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| Challenge: | Existing evidence that high-frequency tokens in pretraining data might bias learning, causing undesired effects, is not clear. |
| Approach: | They propose a sampling algorithm that iteratively assesses token frequencies and removes sentences that contain still high-frequency tokens, resulting in a balanced dataset. |
| Outcome: | The proposed method reduces the amount of pre-training data required for training attention-based transformer language models by up to three times. |
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction (2022.lrec-1)
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Mehdi Mirzapour, Waleed Ragheb, Mohammad Javad Saeedizade, Kevin Cousot, Helene Jacquenet, Lawrence Carbon, Mathieu Lafourcade
| Challenge: | Knowledge graphs are used for information extraction, search engines, question answering, and recommendation systems. |
| Approach: | They propose a French knowledge graph dataset based on RezoJDM. |
| Outcome: | The proposed dataset can be used in many downstream tasks for the French language . it shows that it embeds knowledge graph baselines for link prediction tasks . |
Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation (2025.findings-acl)
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| Challenge: | Dialogue Topic Segmentation (DTS) suffers from data shortage, labeling ambiguity, and incremental complexity of recently proposed solutions. |
| Approach: | They propose a method that employs a structured prompting approach for context summarization, utterance intent classification, and deductive topic shift detection. |
| Outcome: | The proposed method outperforms traditional and state-of-the-art approaches in various dialogue settings. |
Reading Does Not Equal Reading: Comparing, Simulating and Exploiting Reading Behavior across Populations (2024.lrec-main)
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| Challenge: | Existing corpora of eye-tracking-while-reading corporata lack diversity, limiting their ability to include primarily native speakers. |
| Approach: | They expand the eye-tracking-while-reading dataset CopCo by incorporating a new dataset of L2 readers with diverse L1 backgrounds. |
| Outcome: | The extended CopCo corpus comprises neurotypical L1 and L1 readers with dyslexia as well as L2 readers reading the same materials. |