The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels.The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training.This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well.We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions.KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label.KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline.

In this paper, we introduce UniSent universal sentiment lexica for 1000+ languages. Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource languages. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of the number of covered languages, including many low resource ones. In this work, we use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data. We introduce a method called DomDrift to mitigate the huge domain mismatch between Bible and Twitter by a confidence weighting scheme that uses domain-specific embeddings to compare the nearest neighbors for a candidate sentiment word in the source (Bible) and target (Twitter) domain. We evaluate the quality of UniSent in a subset of languages for which manually created ground truth was available, Macedonian, Czech, German, Spanish, and French. We show that the quality of UniSent is comparable to manually created sentiment resources when it is used as the sentiment seed for the task of word sentiment prediction on top of embedding representations. In addition, we show that emoticon sentiments could be reliably predicted in the Twitter domain using only UniSent and monolingual embeddings in German, Spanish, French, and Italian. With the publication of this paper, we release the UniSent sentiment lexica at http://language-lab.info/unisent.

Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language.Resources required for such tasks are often out-of-domain, thus domain adaptation is an important problem here.We make two contributions.First, we test a delightfully simple method for domain adaptation of bilingual word embeddings.We evaluate these embeddings on two bilingual tasks involving different domains: cross-lingual twitter sentiment classification and medical bilingual lexicon induction.Second, we tailor a broadly applicable semi-supervised classification method from computer vision to these tasks.We show that this method also helps in low-resource setups.Using both methods together we achieve large improvements over our baselines, by using only additional unlabeled data.

Nina Seemann, Fabienne Braune, Andreas Maletti. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

In syntax-based machine translation, rule selection is the task of choosing the correct target side of a translation rule among rules with the same source side.We define a discriminative rule selection model for systems that have syntactic annotation on the target language side (stringto-tree).This is a new and clean way to integrate soft source syntactic constraints into string-to-tree systems as features of the rule selection model.We release our implementation as part of Moses.