Improving a Neural-based Tagger for Multiword Expressions Identification (L18-1)
Copied to clipboard
| Challenge: | MUMULS tagger for automatic detection of verbal multiword expressions is based on neural networks . character-level embeddings can improve the performance, reducing out-of-vocabulary rate . multiword Expressions are viewed by computational linguists as a "pain in the neck of NLP" |
| Approach: | They propose to improve MUMULS, a tagger for automatic detection of verbal multiword expressions. |
| Outcome: | The proposed tagger performed better on Czech language than the previous taggers. |
Similar Papers
Deep Neural Representations for Multiword Expressions Detection (2022.acl-srw)
Copied to clipboard
| Challenge: | Existing methods for multiword expression detection are based on sequence labeling and statistical measures. |
| Approach: | They propose a weakly supervised method for multiword expressions extraction . they use a lexicon of English multiword lexical units as a reference knowledge base . |
| Outcome: | The proposed method can be easily applied to other languages. |
TARGER: Neural Argument Mining at Your Fingertips (P19-3)
Copied to clipboard
Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, Alexander Panchenko
| Challenge: | Argumentation is a multi-disciplinary field that extends from philosophy and psychology to linguistics as well as to artificial intelligence. |
| Approach: | They propose to use TARGER to tagging arguments in free text and keyword-based retrieval of arguments from a web-scale corpus. |
| Outcome: | The proposed framework can be used without any reproducibility effort on the user's side and is easily portable to other domains and use cases. |
Multiword Expression aware Neural Machine Translation (2020.lrec-1)
Copied to clipboard
| Challenge: | Multiword Expressions (MWEs) are a pervasive phenomenon in all natural languages and challenge NLP applications because of their unpredictable morpho-syntactic and lexico--semantic behaviour. |
| Approach: | They propose to use linguistic resources to improve MWE translation and MWE generation by up to 5.09 BLEU points on MWE test sets. |
| Outcome: | The proposed annotation and data augmentation improve translation quality and increase performance by up to 5.09 BLEU points on MWE test sets. |
Advances in Pre-Training Distributed Word Representations (L18-1)
Copied to clipboard
| 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. |
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
| Approach: | They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity. |
| Outcome: | The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models. |
Cross-type French Multiword Expression Identification with Pre-trained Masked Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Multiword expressions (MWEs) have linguistic features that distinguish them from regular word groupings. |
| Approach: | They propose a combination of two systems that learn verbal multiword expressions and non-verbal MWEs to improve performance on a cross-type dataset . |
| Outcome: | The proposed system improves the F1 score on a french treebank with VMWEs and nVMWES training data. |
Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging (D18-1)
Copied to clipboard
| Challenge: | Low-resource languages lack manual annotated data to learn basic models such as part-of-speech (POS) taggers. |
| Approach: | They propose a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision in a uniform framework. |
| Outcome: | The proposed model scales to hundreds of low-resource languages without access to gold annotated data. |
Reusing Weights in Subword-Aware Neural Language Models (N18-1)
Copied to clipboard
| Challenge: | a statistical language model assigns a probability to a sequence of words . data sparsity is a major problem in building traditional n-gram language models . |
| Approach: | They propose several ways to reuse subword embeddings and other weights in subword-aware neural language models. |
| Outcome: | The proposed techniques do not benefit a competitive character-aware model . but they show significant reductions in model sizes and performance. |
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to few-shot text classification require domain expertise and an understanding of the language model's abilities to define the mapping between words and labels. |
| Approach: | They propose a method that converts textual inputs to cloze questions that contain some form of task description and processes them with a pretrained language model to map the predicted words to labels. |
| Outcome: | The proposed approach performs almost as well as hand-crafted label-to-word mappings for a number of tasks with small amounts of training data. |
Revisiting Supertagging for faster HPSG parsing (2024.emnlp-main)
Copied to clipboard
| Challenge: | a new supertagger for HPSG-based treebanks is used to improve parsing speed and accuracy. |
| Approach: | They propose to integrate the best supertagger into an HPSG-based parser and compare it to an existing system. |
| Outcome: | The proposed system achieves 97.26% accuracy on 950 sentences from WSJ23 and 93.88% on the out-of-domain technical essay The Cathedral and the Bazaar. |