So you might think the media — and the Democrats — would be all over Brooks and the Republicans, right? Not quite. Zero mentions of him in the national press. Not one story; not one report. There were full days worth of news, takes, and statements regarding whether or not IlhanMN peddled in anti-Semitic tropes while criticizing US policy toward Israel.
Nearly half a million views on this video and virtually no coverage of such blatant Islamophobia in the GOP.
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Shahid told me that he had shared the clip with a number of reporters and producers last week, but none of them had followed up with a story on it. We would now be embarking on another seven or even 70! It would be front-page news; the subject of almost every panel discussion on cable. Yet Brooks makes these outrageous and bigoted claims about Muslims and Islam and …? A shameful and very deafening silence. No headlines. No op-eds. No panels. No reporters chasing down House Republicans and demanding a condemnation or disavowal from them. But what about House Speaker Nancy Pelosi?
Where are the statements of outrage from Chuck Schumer and Steny Hoyer, who were so quick to go after one of their own? As I pointed out last week , anti-Muslim bigotry has been normalized in liberal and Democratic Party circles. Meanwhile, anti-Semitism has been weaponized by Republicans eager to smear their Democratic opponents while simultaneously turning a blind eye to the anti-Semites and white nationalists in their own ranks.
The net result? Omar is hung out to dry while Brooks gets a pass. In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering QA and natural language inference NLI. In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification.
Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i. We can learn a sentiment polarity classifier by optimizing the lower bound.
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We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect. In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: 1 the same word can deliver different emotions in different contexts; 2 some emotions are rarely seen in general dialogues; 3 long-range contextual information is hard to be effectively captured.
Negation scope detection is widely performed as a supervised learning task which relies upon negation labels at word level. This suffers from two key drawbacks: 1 such granular annotations are costly and 2 highly subjective, since, due to the absence of explicit linguistic resolution rules, human annotators often disagree in the perceived negation scopes. To the best of our knowledge, our work presents the first approach that eliminates the need for world-level negation labels, replacing it instead with document-level sentiment annotations.
For this, we present a novel strategy for learning fully interpretable negation rules via weak supervision: we apply reinforcement learning to find a policy that reconstructs negation rules from sentiment predictions at document level. Our experiments demonstrate that our approach for weak supervision can effectively learn negation rules. Furthermore, an out-of-sample evaluation via sentiment analysis reveals consistent improvements of up to 4.
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Moreover, the inferred negation rules are fully interpretable. Unsupervised domain adaptation UDA is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain.
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Unsupervised BWE methods learn such a mapping without any parallel data. However, these methods are mainly evaluated on tasks of word translation or word similarity. We show that these methods fail to capture the sentiment information and do not perform well enough on cross-lingual sentiment analysis. In this work, we propose UBiSE Unsupervised Bilingual Sentiment Embeddings , which learns sentiment-specific word representations for two languages in a common space without any cross-lingual supervision.
Our method only requires a sentiment corpus in the source language and pretrained monolingual word embeddings of both languages. We evaluate our method on three language pairs for cross-lingual sentiment analysis. Experimental results show that our method outperforms previous unsupervised BWE methods and even supervised BWE methods. Our method succeeds for a distant language pair English-Basque. Regularization of neural machine translation is still a significant problem, especially in low-resource settings.
To mollify this problem, we propose regressing word embeddings ReWE as a new regularization technique in a system that is jointly trained to predict the next word in the translation categorical value and its word embedding continuous value. Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.
A desideratum of high-quality translation systems is that they preserve meaning, in the sense that two sentences with different meanings should not translate to one and the same sentence in another language. More generally, translation systems are typically many-to-one non-injective functions from source to target language, which in many cases results in important distinctions in meaning being lost in translation. Building on Bayesian models of informative utterance production, we present a method to define a less ambiguous translation system in terms of an underlying pre-trained neural sequence-to-sequence model.
This method increases injectivity, resulting in greater preservation of meaning as measured by improvement in cycle-consistency, without impeding translation quality measured by BLEU score. We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation NMT. This loss compares original inputs to reconstructed inputs, obtained by back-translating translation hypotheses into the input language. We leverage differentiable sampling and bi-directional NMT to train models end-to-end, without introducing additional parameters.
This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states. Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons.
We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words. The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP.
Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the set of aligned points is allowed to contain an amount of noise, in the form of incorrect lexicon pairs and show that this arises in practice by analyzing the edited dictionaries after the cleaning process. We demonstrate that such noise substantially degrades the accuracy of the learned translation when using current methods. We propose a model that accounts for noisy pairs. This is achieved by introducing a generative model with a compatible iterative EM algorithm.
The algorithm jointly learns the noise level in the lexicon, finds the set of noisy pairs, and learns the mapping between the spaces. We demonstrate the effectiveness of our proposed algorithm on two alignment problems: bilingual word embedding translation, and mapping between diachronic embedding spaces for recovering the semantic shifts of words across time periods. Multilayer architectures are currently the gold standard for large-scale neural machine translation.
Existing works have explored some methods for understanding the hidden representations, however, they have not sought to improve the translation quality rationally according to their understanding.
Towards understanding for performance improvement, we first artificially construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks. Based on our understanding, we then propose to regularize the layer-wise representations with all tree-induced tasks. To overcome the computational bottleneck resulting from the large number of regularization terms, we design efficient approximation methods by selecting a few coarse-to-fine tasks for regularization.
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions.
We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.
Learning high-quality embeddings for rare words is a hard problem because of sparse context information. Mimicking Pinter et al.
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In an evaluation on four tasks, we show that attentive mimicking outperforms previous work for both rare and medium-frequency words. Thus, compared to previous work, attentive mimicking improves embeddings for a much larger part of the vocabulary, including the medium-frequency range. Research in the area of style transfer for text is currently bottlenecked by a lack of standard evaluation practices. This paper aims to alleviate this issue by experimentally identifying best practices with a Yelp sentiment dataset. We specify three aspects of interest style transfer intensity, content preservation, and naturalness and show how to obtain more reliable measures of them from human evaluation than in previous work.