Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings (2020)

Abstract: Emotion lexicons provide information about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving emotion lexicons cross-lingually, especially for low-resource languages, from existing emotion lexicons in resource-rich languages. For this, we start out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced emotion lexicons by measuring translation precision and correlations with existing emotion lexicons, along with measurements on a downstream task of sentence emotion prediction.

## introduction two main forms of classifying emotions are often distinguished: representing them along continuous dimensions, or breaking them into discrete categories @xcite . a prominent instance of the former approach is the pad model by @xcite , which represents affect along 3 dimensions: pleasure, arousal, and dominance. an example of the latter is the wheel of emotions by @xcite , who argued that most emotions can be derived from a set of eight basic ones -anger, fear, sadness, disgust, surprise, anticipation, trust, and joy. there have been efforts to create emotion lexicons, where each word is assigned either scores or discrete classes reflecting the associated emotions. such lexicons are useful in emotional analyses of product reviews, literary texts, or posts on social media, inter alia. @xcite solicited human affective norm ratings to create such a dataset for english based on the pad model. @xcite relied on crowdsourcing to annotate words with plutchik's 8 basic emotions, providing binary labels. the recent nrc emotion intensity lexicon @xcite reconciles the notion of discrete emotions, corresponding to commonly invoked emotion names, with the benefits of continuous scoring in accounting for degrees of emotion intensity. again relying on crowdsourcing, the lexicon provides intensity scores for plutchik's eight basic emotions. affective norm ratings have as well been procured for certain other languages. an alternative route is to draw on automated techniques such as machine translation, as has been done for the nrc emotion intensity lexicon, where the english words are translated to other languages using google translate while retaining the original scores. @xcite used google translate to translate a source emotion lexicon to a target lexicon that serves as training data, based on which valence/arousal/dominance or 5 basic emotions are predicted for a range of resource-rich languages. however, at the time of writing this paper, google translate serves around 100 languages. this raises the question of whether similar resources can be induced for resource-poor languages using minuscule amounts of data. in this paper, we investigate simple means of deriving emotion ratings for resource-poor languages. in particular, we consider the case of drawing on very small corpora, focusing on partial translations of the bible. we explore different cross-lingual embedding alignment techniques that allow us to transfer english emotion ratings to over 350 languages, assessing the accuracy of translations and of our induced emotion ratings. we have made the resulting induced emotion lexicons freely available foot_0 . ## proposed method in this section, we introduce some pertinent definitions and provide a brief overview of our methodology to induce emotion ratings for resource-poor languages. we consider a target language l t that is typically a resource-poor one, for which no emotion ratings are available, and a source language l s , for which emotion ratings are available. we define an emotion rating σ e (w) ∈ [0, 1] as an emotion intensity score, i.e., the degree of emotional association of word w with emotion @xmath0. accordingly, an emotion lexicon e can be regarded as a function of the form v × @xmath1, 1] that maps words w from a vocabulary v paired with emotions @xmath2. our method requires three resources: a monolingual text corpus for each of l t and l s , and an emotion lexicon e s for the source language. we induce a target-language e t in a two-step process: first, we induce a cross-lingual word embeddings space covering both l t and l s , by drawing on the monolingual corpora as well as unsupervised cross-lingual alignment (section 4). subsequently, we derive emotion ratings for l t using this vector space, based on the source lexicon e s (section 5). our empirical investigations focus mainly on the first step. we evaluate three algorithms to induce cross-lingual word embeddings in such low-resource settings. we also explore additional supervision when the input corpora possess sentence-level alignments, that is, information about which sentences in l t are translations of which sentences in l s . this is the case for the bible translations considered in this study, due to the presence of verse identifiers. ## cross-lingual embedding induction in this section, we explain our overall approach to obtain cross-lingually aligned word embeddings, and then briefly outline three of the algorithms we use for alignment along with our modifications. ## cross-lingual emotion rating induction equipped with our cross-lingual embedding space x c , we are now able to induce emotion ratings crosslingually based on the source language emotion lexicon e s . for each target language word @xmath3, we compute a score the advantage of csls over simple cosine similarities is that it compensates for hubness, the property that some vectors in an embedding space reside near overly many other vectors @xcite . it achieves this by subtracting hubness factors r l 1 (v 2 ) and r l 2 (v 1 ) for v 1 , v 2 , where r l i (v) yields the average cosine similarity of the @xmath4. ## experimental setup in the following, we present an empirical analysis of the feasibility of inducing emotion ratings using the above methods when drawing on very small monolingual corpora. we first present our data sources (section 6.1) and algorithmic parameters (section 6.2), and then discuss various methods of measurement to verify the effectiveness of our methods (section 6.3). the results follow in section 7. the number of sentences in each language is in the table header, below each language code. the rest of the layout is similar to ## results we present the evaluation of cross-lingual embeddings in section 7.1 and of the resulting emotion ratings in section 7.2. additionally, we conduct a case study on sentence-level emotion ratings in section 7.4. ## conclusion in this paper, we investigate approaches to cross-lingually induce emotion ratings based on very small training corpora. this is achieved by taking an emotion lexicon for a resource-rich language and inducing a cross-lingual embedding space to transfer the source language emotion ratings to words in the resourcepoor target languages. we compare several strategies to achieve this and evaluate them in terms of both translation precision and the final correlation of the induced emotion ratings with existing emotion lexicons. generally, we find that our modified variants of the original algorithms yield important gains in such low-resource settings. we also evaluate them on the downstream task of unsupervised sentencelevel emotion prediction on a human-annotated literary corpus. overall, while the methods do not work sufficiently well with 10k or fewer verses, we attained satisfactory results on languages for which translated bibles with at least 31k verses exist. this still leaves us with the ability to induce cross-lingual emotion ratings for over 350 languages, available online at http://emotionlexicon.org/ .