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A Representation Learning Framework for Multi-Source Transfer Parsing

AAAI Conferences

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target language-specific syntactic structures are difficult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2.0), our best models yield an absolute improvement of 6.53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently.


Amazon researchers reduce data required for AI transfer learning

#artificialintelligence

Cross-lingual learning is an AI technique involving training a natural language processing model in one language and retraining it in another. It's been demonstrated that retrained models can outperform those trained from scratch in the second language, which is likely why researchers at Amazon's Alexa division are investing considerable time investigating them. In a paper scheduled to be presented at this year's Conference on Empirical Methods in Natural Language Processing, two scientists at the Alexa AI natural understanding group -- Quynh Do and Judith Gaspers -- and colleagues propose a data selection technique that halves the amount of required training data. They claim that it surprisingly improves rather than compromises the model's overall performance in the target language. "Sometimes the data in the source language is so abundant that using all of it to train a transfer model would be impractically time consuming," wrote Do and Gaspers in a blog post.


Guo

AAAI Conferences

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target language-specific syntactic structures are difficult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2.0), our best models yield an absolute improvement of 6.53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently.


Unsupervised Text Style Transfer using Language Models as Discriminators

Neural Information Processing Systems

Binary classifiers are employed as discriminators in GAN-based unsupervised style transfer models to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with the binary discriminator is that error signal is sometimes insufficient to train the model to produce rich-structured language. In this paper, we propose a technique of using a target domain language model as the discriminator to provide richer, token-level feedback during the learning process. Because our language model scores sentences directly using a product of locally normalized probabilities, it offers more stable and more useful training signal to the generator. We train the generator to minimize the negative log likelihood (NLL) of generated sentences evaluated by a language model.


Fighting offensive language on social media with unsupervised text style transfer

#artificialintelligence

Online social media has become one of the most important ways to communicate and exchange ideas. Unfortunately, the discourse is often crippled by abusive language that can have damaging effects on social media users. Online social media networks normally deal with the offensive language problem by simply filtering out a post when it is flagged as offensive. In the paper "Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer," which was presented in the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), we introduce a completely new approach to tackle this problem. Our approach uses unsupervised text style transfer to translate offensive sentences into corresponding non-offensive forms.