| Challenge: | Existing methods for complex dialog management require limited training data. |
| Approach: | They propose a method for intent recognition for complex dialog management in low resource situations . they use windowed word n-grams, POS tag n grams and pre-trained word embeddings as features . |
| Outcome: | The proposed method performs better with less than 1% of the data size than existing methods but requires considerably more data. |
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Augmented Natural Language for Generative Sequence Labeling (2020.emnlp-main)
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| Challenge: | generative framework for joint sequence labeling and sentence-level classification is general purpose, performing well on few-shot learning, low resource, and high resource tasks. |
| Approach: | They propose a generative framework for joint sequence labeling and sentence-level classification . their framework incorporates label semantics and shares knowledge across tasks . |
| Outcome: | The proposed model performs on few-shot learning, slot labeling, and intent classification benchmarks. |
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification (2023.findings-emnlp)
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| Challenge: | Semi-supervised methods for detecting intent generate a large amount of unlabeled data . labeling data requires substantial human effort, and picking an imbalanced set of examples could lead to poor labels. |
| Approach: | They propose a balanced distance-based pseudo-labeling approach for semisupervised intent classification . they use a ranking-based approach to select samples with a model prediction confidence . |
| Outcome: | The proposed method outperforms existing models on popular datasets. |
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification (2022.acl-short)
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| Challenge: | Existing methods for multi-label text classification neglect the knowledge from the existing similar instances when predicting labels of a specific text. |
| Approach: | They propose a k nearest neighbor mechanism which retrieves several neighbor instances and interpolates the model output with their labels. |
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A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling (P18-1)
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| Challenge: | Existing studies have shown that multi-task learning can boost the performance of related tasks such as MT and abstractive text summarization. |
| Approach: | They propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. |
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GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| Challenge: | Existing sequence labeling algorithms can be decomposed into two parts . |
| Approach: | They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. |
| Outcome: | The proposed model performs well on three sequence labeling tasks. |
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)
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| Challenge: | Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER. |
| Approach: | They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain. |
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Intent Features for Rich Natural Language Understanding (2021.naacl-industry)
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| Challenge: | generic dialog systems, or chatbots, are increasingly popular, but most industrial dialog systems are built for specific clients and use cases. |
| Approach: | They propose a new neural network architecture that allows for domain and topic agnostic properties of intents that can be learnt from syntactic cues only. |
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Attending to Long-Distance Document Context for Sequence Labeling (2020.findings-emnlp)
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| Challenge: | UC Berkeley researchers develop a method for incorporating global context in long documents . many of the main datasets used in NLP are comprised of relatively short documents - english OntoNotes contains 223 tokens . |
| Approach: | They propose a method for incorporating global context in long documents . they use multiple mentions of the same word type to generate a representation for each token . |
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Few-shot Pseudo-Labeling for Intent Detection (2020.coling-main)
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| Challenge: | Existing methods for labeling intents are expensive and time-consuming. |
| Approach: | They propose a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. |
| Outcome: | The proposed method performs better on multiple intent detection datasets and is stronger than existing methods. |
More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
| Outcome: | The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders. |