Papers with Natural Language Processing community
Efficient Transformer Knowledge Distillation: A Performance Review (2023.emnlp-industry)
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| Challenge: | Pretrained transformer language models have been gaining popularity in the field of natural language processing . however, there is no study into the intersection of these two fields . |
| Approach: | They propose a method to extract knowledge from transformers to produce high-performing efficient attention models with low costs. |
| Outcome: | The proposed model compression method preserves up to 98.6% of original model performance across short-context tasks and up to 95.8% on long-concept Named Entity Recognition tasks while decreasing inference times by up to 57%. |
SPRING Goes Online: End-to-End AMR Parsing and Generation (2021.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for representing the semantics of natural language in a readable and hierarchical way. |
| Approach: | They present SPRING Online Services, a Web interface and RESTful APIs for their AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). |
| Outcome: | The proposed system provides a highly interactive visualization platform and feedback mechanism to obtain user suggestions for further improvements of the system’s output. |
LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization (2023.eacl-main)
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| Challenge: | Text Summarization is a popular task and a challenge for neural models. |
| Approach: | They propose to exploit visual/layout information to capture long-range dependencies in summarization models by combining layout-aware and long-reaching models. |
| Outcome: | The proposed datasets cover French, Spanish, Portuguese, and Korean languages. |
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (P19-1)
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| Challenge: | Word embeddings suffer from unintended demographic biases, a new study shows . word embedders can cause downstream NLP systems to be unfair, the authors argue . |
| Approach: | They propose a metric to evaluate the fairness of word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
| Outcome: | The proposed metric measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network (C18-1)
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| Challenge: | Existing studies on question answer matching focus on formal text . however, there exists many scenarios where the QA text is informal . |
| Approach: | They propose a novel QA matching approach using informal text from a product review site. |
| Outcome: | The proposed approach improves word-level and sentence-level attentions for solving the noisy problem in the informal text. |
Two Multilingual Corpora Extracted from the Tenders Electronic Daily for Machine Learning and Machine Translation Applications. (L18-1)
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| Challenge: | European "Tenders Electronic Daily" is a valuable source of semi-structured and multilingual data . collecting and managing such kind of data is incredibly burdensome and takes time and resources . |
| Approach: | They describe two documented and easy-to-use multilingual corpora extracted from the TED web site . they propose to make the extracted dataset available to the scientific community . |
| Outcome: | The proposed dataset is based on the European tenders electronic daily (TED) web site . it is easy to use and can be used for text mining and natural language processing tasks. |
VisToT: Vision-Augmented Table-to-Text Generation (2022.emnlp-main)
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| Challenge: | Existing models for data-to-text generation are wrongly generating estate in the output text. |
| Approach: | They propose a task that incorporates visual cues from tables and associated images to generate relevant text. |
| Outcome: | The proposed task incorporates visual cues from tables and associated images to generate relevant text. |
MultiEMO: An Attention-Based Correlation-Aware Multimodal Fusion Framework for Emotion Recognition in Conversations (2023.acl-long)
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| Challenge: | Emotion Recognition in Conversations (ERC) is an increasingly popular task in the field of Natural Language Processing. |
| Approach: | They propose a framework that captures cross-modal mapping relationships across modalities . they propose 'multiemotion-aware' framework that integrates multimodal cues into the model . |
| Outcome: | The proposed framework outperforms state-of-the-art models in all emotion categories on two benchmark datasets. |
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups (2024.emnlp-main)
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Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
| Challenge: | Large language models (LLMs) are popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. |
| Approach: | They investigate the use of large language models in CWI, LCP, and MWE settings by evaluating their use in zero-shot, few-shot and fine-tuning settings. |
| Outcome: | The proposed models struggle in certain conditions or achieve comparable results against existing methods. |
ComicScene154: A Scene Dataset for Comic Analysis (2025.emnlp-main)
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| Challenge: | Comics offer compelling yet under-explored domain for computational narrative analysis . authors highlight potential of comics for narrative-driven, multimodal data analysis based on novel comics . |
| Approach: | They propose a dataset of scene-level narrative arcs derived from comic books . they highlight their potential to inform broader research on multimodal storytelling . |
| Outcome: | The dataset provides an initial benchmark that future studies can build upon. |