Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing (N18-1)
Copied to clipboard
| Challenge: | a few researchers have shown that data traces from human processing can be used to improve NLP models. |
| Approach: | They propose to use data readily available for most languages to improve unsupervised induction . they find that english unsupervised POS induction achieves an error reduction of 1.5% . |
| Outcome: | The proposed model improves on Ontonotes domains with a word embeddings. |
Similar Papers
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages (N19-1)
Copied to clipboard
| Challenge: | Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but taggers need to ground their clusters as well. |
| Approach: | They propose an approach for low-resource unsupervised part of speech (POS) tagging that yields fully grounded output and requires no labeled training data. |
| Outcome: | The proposed method achieves reasonable performance across languages, including Sinhalese and Kinyarwanda, with no labeled training data. |
On the Limitations of Unsupervised Bilingual Dictionary Induction (P18-1)
Copied to clipboard
| Challenge: | Unsupervised machine translation does not require cross-lingual supervision, whether a dictionary, translations, or comparable corpora. |
| Approach: | They propose an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction that exploits a weak supervision signal from identical words. |
| Outcome: | The proposed model relies heavily on an adversarial, unsupervised cross-lingual word embedding technique for bilingual dictionary induction. |
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. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
Copied to clipboard
| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
| Outcome: | The proposed approach outperforms existing methods in bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing tasks. |
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios (2020.emnlp-main)
Copied to clipboard
| Challenge: | a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance . |
| Approach: | They propose an unsupervised cross-lingual transfer approach for part-of-speech tagging . they use the Bible as parallel data to learn POS taggers for target languages . |
| Outcome: | The proposed approach improves accuracy on 12 diverse languages . the Bible is used as a parallel corpus for the study . |
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for predicting linguistic structures require labeled data . unsupervised chunking is useful for understanding linguistic structure of human languages . |
| Approach: | They propose a knowledge-transfer approach that heuristically induces chunk labels from unsupervised parsing models and a hierarchical recurrent neural network (HRNN) they show that their approach bridges the gap between supervised and unsupervised chunking. |
| Outcome: | The proposed method bridges the gap between supervised and unsupervised chunking. |
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? (D19-1)
Copied to clipboard
| Challenge: | a series of bilingual lexicon induction experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods fail for a large number of language pairs. |
| Approach: | They propose to use fully unsupervised approaches to project monolingual embeddings into a shared cross-lingual space without any cross-linguistic signal. |
| Outcome: | The proposed methods fail for a large number of language pairs, but never surpass weakly supervised methods. |
Learning Word Embeddings for Low-Resource Languages by PU Learning (N18-1)
Copied to clipboard
| Challenge: | Existing approaches to learn word embedding on a corpus with only a few million tokens are limited to low-resource languages. |
| Approach: | They propose to use a sparse co-occurrence matrix to factorize the co-existence matrix and validate the proposed approaches in four different languages. |
| Outcome: | The proposed model is validated in four different languages. |
Improved Latent Tree Induction with Distant Supervision via Span Constraints (2021.emnlp-main)
Copied to clipboard
Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee, Tim O’Gorman, Subendhu Rongali, Dylan Finkbeiner, Shilpa Suresh, Mohit Iyyer, Andrew McCallum
| Challenge: | Distant supervision is not a practical way to perform unsupervised syntactic parsing. |
| Approach: | They propose a technique that uses distant supervision to improve unsupervised constituency parsing by using phrase bracketing. |
| Outcome: | The proposed method improves constituency parsing on English WSJ Penn Treebank by more than 5 F1 compared with full parse tree annotations. |