| Challenge: | Recent advances in word embeddings have proven successful in learning entity representations from short texts but do not capture full book-level information. |
| Approach: | They propose two novel ways to learn fixed-length vector representations of characters from novels . they use graph neural network-based embeddings from a full corpus-based character network . |
| Outcome: | The proposed methods outperform text-based embeddings in four tasks. |
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Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)
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| Challenge: | Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics. |
| Approach: | They compare graph representation methods for text classification using different architectures and setups. |
| Outcome: | The proposed graph representation methods outperform other models in document comprehension tasks. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Story Embeddings — Narrative-Focused Representations of Fictional Stories (2024.emnlp-main)
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| Challenge: | Existing approaches to model fictional narratives have focused on the aspect of "what" rather than "how" they are being told. |
| Approach: | They propose a model that embeds stories such that similar stories will result in similar embeddings. |
| Outcome: | The proposed model shows state-of-the-art performance on multiple retrieval tasks and a narrative understanding task. |
Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
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| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
| Outcome: | The proposed method leads to state-of-the-art performance on a variety of tasks. |
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the CHATGPT Era and Beyond (2024.eacl-long)
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Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Viktor Schlegel, Stefan Winkler, See-Kiong Ng, Soujanya Poria
| Challenge: | Sentence representations are a critical component in NLP applications such as retrieval, question answering, and text classification. |
| Approach: | They present a systematic review of the literature on sentence representations focusing mostly on deep learning models. |
| Outcome: | The proposed methods highlight the key contributions and challenges in this area and suggest potential avenues for improving the quality and efficiency of sentence representations. |
Improving Quotation Attribution with Fictional Character Embeddings (2024.findings-emnlp)
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| Challenge: | Recent methods to attribute quotes to human logic lack character representations, which often leads to errors in more challenging examples of attribution: anaphoric and implicit quotes. |
| Approach: | They propose to augment a popular quotation attribution system, BookNLP, with character embeddings that encode global stylistic information of characters derived from an off-the-shelf stylometric model, Universal Authorship Representation (UAR). |
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Evaluation of Sentence Representations in Polish (2020.lrec-1)
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| Challenge: | Existing methods for learning sentence representations have been limited in low-resource languages such as Polish . |
| Approach: | They propose two new Polish datasets for evaluating sentence embeddings and evaluate eight different methods including Polish and multilingual models. |
| Outcome: | The proposed methods show strengths and weaknesses in Polish and multilingual models. |
Learning Visually Grounded Sentence Representations (N18-1)
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
| Approach: | They train a sentence encoder to predict image features of a caption and use them as sentence representations. |
| Outcome: | The proposed model improves on word embeddings and word representations on standard benchmarks. |
Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |