Shrinking Japanese Morphological Analyzers With Neural Networks and Semi-supervised Learning (N19-1)
Copied to clipboard
| Challenge: | Modern neural morphological analyzers consume gigabytes of memory. |
| Approach: | They propose a method which uses unigram character embeddings to train a model on labels produced by a state-of-the-art analyzer. |
| Outcome: | The proposed model outperforms dictionary-based methods in Japanese and Chinese . it uses less than 15 megabytes of space and is much smaller than the dictionary- based one . |
Similar Papers
Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie (2023.acl-short)
Copied to clipboard
| Challenge: | Accurate neural models are less efficient than non-neural models and are useless for processing billions of social media posts and handling user queries. |
| Approach: | They propose to make fast pattern-based NLP methods as accurate as possible . they propose a morphological analyzer for Japanese that induces reliable patterns . |
| Outcome: | The proposed method induces reliable patterns from a morphological dictionary and annotated data in Japanese. |
Juman++: A Morphological Analysis Toolkit for Scriptio Continua (D18-2)
Copied to clipboard
| Challenge: | a morphological analyzer is useful for languages without natural word boundaries, but it is difficult to improve it without creating costly annotations. |
| Approach: | They propose a toolkit for developing morphological analyzers for languages without natural word boundaries using lattices and neural nets. |
| Outcome: | The proposed morphological analyzer of Japanese achieves new SOTA on Jumandic-based corpora while being 250 times faster than the previous one. |
Morphological Segmentation for Low Resource Languages (2020.lrec-1)
Copied to clipboard
Justin Mott, Ann Bies, Stephanie Strassel, Jordan Kodner, Caitlin Richter, Hongzhi Xu, Mitchell Marcus
| Challenge: | a new corpus of annotated morphological data is described for the DARPA LORELEI Program . the data is annotating 9 low resource languages and root information for 7 of the languages . |
| Approach: | This paper describes a new morphology resource created by Linguistic Data Consortium and the University of Pennsylvania for the DARPA LORELEI Program. |
| Outcome: | The annotated corpus provides a gold standard for unsupervised morphological segmenters and analyzers . the language-specific annotation guidelines were language-independent, but included morphology paradigms and other specifications. |
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages (N18-1)
Copied to clipboard
| Challenge: | Morphological segmentation for polysynthetic languages is challenging because of limited training data. |
| Approach: | They propose two new multi-task training approaches that improve performance for Mexican polysynthetic languages . they also propose cross-lingual transfer as a third way to fortify their neural model . |
| Outcome: | The proposed models improve on Mexicanero, Nahuatl, Wixarika and Yorem Nokki . the proposed models reduce the amount of parameters by close to 75% . |
Bootstrapping Techniques for Polysynthetic Morphological Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Polysynthetic languages have exceptionally large and sparse vocabularies due to the number of morpheme slots and combinations in a word. |
| Approach: | They propose linguistically-informed approaches for bootstrapping a neural morphological analyzer . they use a finite state transducer to train an encoder-decoder model . |
| Outcome: | The proposed method improves on a polysynthetic language's model by "hallucinating" missing linguistic structure and resampling from a Zipf distribution to simulate a more natural distribution of morphemes. |
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to morphological analysis relied on hand-built rules to identify word-internal structures. |
| Approach: | They propose a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphology. |
| Outcome: | The proposed model outperforms existing systems on nine typologically and genetically diverse languages and shows superior performance over leading systems. |
Using Morphological Knowledge in Open-Vocabulary Neural Language Models (N18-1)
Copied to clipboard
| Challenge: | Existing models that generate words from a fixed vocabulary are linguistically nave . authors present an open-vocabulary language model that incorporates morphological knowledge into a neural framework . |
| Approach: | They propose a model that incorporates morphological knowledge into a neural model by generating words as a sequence of characters, generating full word forms and combining them with a hand-written morphology analyzer. |
| Outcome: | The proposed model outperforms character-based models on Finnish, Turkish, and Russian on three languages. |
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors (2021.findings-acl)
Copied to clipboard
Ramy Eskander, Cass Lowry, Sujay Khandagale, Francesca Callejas, Judith Klavans, Maria Polinsky, Smaranda Muresan
| Challenge: | Unsupervised morphological segmentation is an essential subtask in many natural language processing applications. |
| Approach: | They introduce two types of priors: grammar definition and linguist-provided affixes . they show that priors boost morphological segmentation performance in a minimally-supervised manner . |
| Outcome: | The proposed priors achieve 8.9% and 34.2% error reductions over the state-of-the-art unsupervised system. |
A Japanese Word Segmentation Proposal (P19-2)
Copied to clipboard
| Challenge: | Current word segmentation methods may produce different segmentations for the same strings . this occurs when strings appear in different sentences . |
| Approach: | They propose to use Japanese word segmentation methods that use a morpheme-based approach to produce different segmentations for the same strings. |
| Outcome: | The proposed method produces much more consistent segmentation than the current morpheme-based one. |
Better Character Language Modeling through Morphology (P19-1)
Copied to clipboard
| Challenge: | Inflected words benefit more from explicitly modeling morphology than uninflectes . morphological supervision is also used to augment character language models in low-resource languages . |
| Approach: | They add morphological supervision to character language models via multitasking to improve BPC performance across 24 languages even when morphology data and language modeling data are disjointed. |
| Outcome: | The addition improves performance even when morphology data and language modeling data are disjointed. |