| Challenge: | Existing methods for retrieving documents using vectors have been used to model documents and queries using bag-of-words (BOW) representations. |
| Approach: | They propose to use the word embeddings of a document to define a novel document descriptor. |
| Outcome: | The proposed descriptor performs well against state-of-the-art methods in supervised and unsupervised environments. |
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| Challenge: | a novel word embedding representation for text documents is proposed . the method is based on the Vector of Locally-Aggregated Descriptors used for image representation . |
| Approach: | They propose a novel representation for text documents based on aggregating word embedding vectors into document embeddables. |
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Sentiment Classification Using Document Embeddings Trained with Cosine Similarity (P19-2)
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| Challenge: | Existing document embedding models map each document to a dense, low-dimensional vector in continuous vector space. |
| Approach: | They propose to train document embeddings using cosine similarity instead of dot product . they focus on document sentiment classification of long movie reviews . |
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Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing (N19-1)
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| Challenge: | Existing methods to estimate document similarity based on word embeddings are mediocre . a recent study compared word and sentence embedded documents to a similarity estimate using matrix norms. |
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Self-Discriminative Learning for Unsupervised Document Embedding (N19-1)
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| Challenge: | Existing methods for document embedding learning do not consider inter-document relationships. |
| Approach: | They propose to exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. |
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Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
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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. |
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Efficient Classification of Long Documents Using Transformers (2022.acl-short)
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| Challenge: | Several methods have been proposed for classifying long textual documents using Transformers, but there is a lack of consensus on a benchmark to enable a fair comparison among different approaches. |
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Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis (2023.findings-eacl)
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Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
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| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
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Pseudo-Relevance for Enhancing Document Representation (2022.emnlp-main)
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| Challenge: | a novel approach to document retrieval can be used to encode documents as vectors . a few query-relevant terms can be pruned out to reduce index overhead . |
| Approach: | They propose to enhance the document representation for the bi-encoder approach in dense document retrieval. |
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