Papers with Part-Of-Speech
A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging (2022.coling-1)
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| Challenge: | Dialogue Act tagging with ISO 24617-2 standard is a difficult task that requires multiple labels covering semantic, syntactic and pragmatic aspects of dialogue. |
| Approach: | They propose a neural architecture to increase Dialogue Act tagging accuracy by using low-frequency fine-grained tags. |
| Outcome: | The proposed model achieves state-of-the-art tagging results on DialogBank data set . it uses syntactic information in the form of Part-Of-Speech and dependency tags . |
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? (2022.naacl-main)
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| Challenge: | Recent Part-Of-Speech (POS) induction models assume certain independence assumptions that do not hold in real languages. |
| Approach: | They propose a Masked Part-of-Speech Model (MPoSM) that can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
| Outcome: | The proposed model can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. |
Unsupervised Abstractive Summarization of Bengali Text Documents (2021.eacl-main)
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Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat
| Challenge: | Abstractive summarization systems are difficult to perform due to the unavailability of the parallel data for low-resource languages like Bengali. |
| Approach: | They propose a graph-based unsupervised abstractive summarization system in Bengali text documents that requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. |
| Outcome: | The proposed system outperforms baselines without human-annotated reference summaries on a human-random dataset with Bengali text. |
LIMIT-BERT : Linguistics Informed Multi-Task BERT (2020.findings-emnlp)
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| Challenge: | Existing language models are usually trained on large amounts of unlabeled text data. |
| Approach: | They propose a multi-task language representations learning framework for multi-linguistics tasks by Multi-Task Learning. |
| Outcome: | The proposed model outperforms the baseline Whole Word Masking BERT on both dependency and constituent syntactic/semantic parsing, GLUE benchmark, and SNLI task. |
A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts (L18-1)
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| Challenge: | Recent approaches based on end-to-end Deep Neural Networks (DNNs) have shown promising results for Natural Language Processing (NLP). |
| Approach: | They propose a neural network model for part-of-speech (POS) tagging of User-Generated Content (UGC) such as Twitter, Facebook and Web forums that uses character and word representations. |
| Outcome: | The proposed model is end-to-end and uses character and word representations . it is compared with existing models on social media in English, german, french, italian and spanish . |