Papers with data sparsity problem
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions (2020.acl-srw)
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| Challenge: | Parallel corpora are key to developing good machine translation systems, but abundant parallel data is hard to come by for languages with a low number of speakers. |
| Approach: | They propose an unsupervised alignment method that can handle rich morphology by removing incorrect translations and segments containing extraneous data. |
| Outcome: | The proposed method maximizes the number of correctly translated segments in a corpus and minimises noise by removing incorrect translations and segments containing extraneous data. |
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling (2021.findings-acl)
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| Challenge: | Standard sequence labeling systems fail when processing noisy user-generated text or consuming the output of an OCR process. |
| Approach: | They propose an empirical error generation approach that employs a sequence-to-sequence model trained to perform translation from error-free to erroneous text. |
| Outcome: | The proposed method outperforms baseline noise generation and error correction techniques on the erroneous sequence labeling data sets. |
Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)
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| Challenge: | Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance . |
| Approach: | They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs . |
| Outcome: | The proposed model outperforms benchmark models on multiple public datasets. |
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)
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| Challenge: | Existing methods to track dialogue state are limited due to data sparsity and long dialogues. |
| Approach: | They propose to use the previous dialogue state and current dialogue utterance as input for DST. |
| Outcome: | The proposed approach outperforms existing methods and improves existing ones. |
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization (D18-1)
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| Challenge: | Existing methods for categorization of short texts use non-hierarchical flat model, but they are limited by domain-independent knowledge distribution. |
| Approach: | They propose a method which leverages hierarchical relationships between pre-defined categories to tackle the data sparsity problem. |
| Outcome: | The proposed method is competitive with the state-of-the-art methods on a multi-label categorization task for short texts using two benchmark datasets. |
Extracting Parallel Sentences with Bidirectional Recurrent Neural Networks to Improve Machine Translation (C18-1)
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| Challenge: | Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. |
| Approach: | They propose a bidirectional recurrent neural network based approach to extract parallel sentences from multilingual corpora. |
| Outcome: | The proposed approach outperforms existing approaches on noisy parallel corpora and shows significant improvements in translation performance. |
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation (2024.acl-long)
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| Challenge: | Existing conversational dense retrieval models view a conversation as a fixed sequence of questions and responses, and these alternate conversations are unrecorded. |
| Approach: | They propose a framework for generalizing Conversational dense retrieval via LLM-cognition data Augmentation (ConvAug) they first generate multi-level augmented conversations to capture the diverse nature of conversational contexts. |
| Outcome: | The proposed framework generalizes Conversational dense retrieval via LLM-cognition data Augmentation on four public datasets. |
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts (N18-1)
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| Challenge: | Existing keyphrase extraction methods suffer from data sparsity problem when conducted on short and informal texts. |
| Approach: | They propose a neural keyphrase extraction framework for microblog posts that takes conversation context into account and uses four types of neural encoders to represent conversation context. |
| Outcome: | The proposed framework outperforms state-of-the-art keyphrase extraction methods on Twitter and Weibo datasets. |
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)
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| Challenge: | Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems . |
| Approach: | They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed. |
| Outcome: | The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data. |
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA (2022.findings-emnlp)
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| Challenge: | Existing methods to retrieve evidences from corpus are difficult due to table-text discrepancy and data sparsity problem. |
| Approach: | They propose an optimized OpenQA Table-Text Retriever to retrieve tabular and textual evidences from tabular resources. |
| Outcome: | The proposed OpenQA Table-Text Retriever significantly outperforms existing methods on QA tasks. |
Neural News Recommendation with Heterogeneous User Behavior (D19-1)
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| Challenge: | Existing news recommendation methods rely on news click history to model user interest, but data sparsity is a problem . other kinds of user behaviors such as webpage browsing and search queries can provide useful clues of users’ news reading interest. |
| Approach: | They propose to exploit heterogeneous user behaviors to learn news representations from their titles via CNN networks and apply attention networks to select important words. |
| Outcome: | The proposed approach exploits heterogeneous user behaviors on a real-world dataset. |
Handling Rare Entities for Neural Sequence Labeling (2020.acl-main)
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| Challenge: | Recent approaches to sequence labeling have been based on statistical models but a challenge is from the data sparsity problem. |
| Approach: | They propose to use local context reconstruction to implicitly incorporate contextual information into their representations. |
| Outcome: | The proposed model outperforms all previous methods on multiple benchmark datasets and achieves new start-of-the-art results. |
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories (2021.emnlp-main)
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| Challenge: | Existing supervised models struggle to make correct predictions on rare word senses due to limited training data. |
| Approach: | They propose a gloss alignment algorithm that can align definition sentences with the same meaning from different sense inventories to collect rich lexical knowledge. |
| Outcome: | The proposed method outperforms previous methods on both frequent and rare word senses. |