Construction of a Corpus of Rhetorical Devices in Slogans and Structural Analysis of Antitheses

An advertising slogan is a sentence that expresses a product or a work of art in a straightforward manner and is used for advertising and publicity. Moving the consumer's mind and attracting their interest can significantly influence sales. Although rhetorical techniques in a slogan are known to improve the effectiveness of advertising, not much attention has been devoted to analyze or automatically generate sentences with the techniques. Therefore, we constructed a large corpus of slogans and revealed the linguistic characteristics of the basic statistics and rhetorical devices. Another point of focus was antitheses, of which the usage rates are relatively high and which have a specific sentence structure and lexical constraints. The generation of a slogan that contains an antithesis necessitates the structure of sentences, known as templates, to be extracted and also requires knowledge of word pairs with semantic contrast. Thus, the next step involved analysis of the structure to extract the sentence structure and lexical knowledge about the antithesis. Despite its simple architecture, the proposed method exceeds the prediction accuracy and efficiency of a comparable method. Lexical knowledge that is not available in existing dictionaries was also extracted.

EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints

We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.

Alignment-Enhanced Transformer for Constraining NMT with Pre-Specified Translations

We investigate the task of constraining NMT with pre-specified translations, which has practical significance for a number of research and industrial applications. Existing works impose pre-specified translations as lexical constraints during decoding, which are based on word alignments derived from target-to-source attention weights. However, multiple recent studies have found that word alignment derived from generic attention heads in the Transformer is unreliable. We address this problem by introducing a dedicated head in the multi-head Transformer architecture to capture external supervision signals. Results on five language pairs show that our method is highly effective in constraining NMT with pre-specified translations, consistently outperforming previous methods in translation quality.

Functional and anaphoric control

This chapter explores the syntax and semantics of functional and anaphoric control, constructions in which either syntactic or lexical constraints require coreference between an argument of the matrix clause (the controller) and an argument of a subordinate or modifying adjunct clause (the controllee). Such cases include the classes of “raising” verbs (Section 15.2) and “equi” verbs (Section 15.4). Crosslinguistically, descriptions of such constructions involve reference to functional syntactic relations such as subject and object; therefore, the syntactic discussion in this chapter is primarily centered around the f-structures of functional and anaphoric control constructions. A detailed semantic analyses of functional and anaphoric control constructions is also presented, considering arbitrary, obligatory, and quasi-obligatory (partial) control relations, and a discussion of the syntax and semantics of control in adjuncts (Section 15.8).

PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-Constrained Neural Machine Translation

We present PARABANK, a large-scale English paraphrase dataset that surpasses prior work in both quantity and quality. Following the approach of PARANMT (Wieting and Gimpel, 2018), we train a Czech-English neural machine translation (NMT) system to generate novel paraphrases of English reference sentences. By adding lexical constraints to the NMT decoding procedure, however, we are able to produce multiple high-quality sentential paraphrases per source sentence, yielding an English paraphrase resource with more than 4 billion generated tokens and exhibiting greater lexical diversity. Using human judgments, we also demonstrate that PARABANK’s paraphrases improve over PARANMT on both semantic similarity and fluency. Finally, we use PARABANK to train a monolingual NMT model with the same support for lexically-constrained decoding for sentence rewriting tasks.

Learning a Frequency-Matching Grammar together with Lexical Idiosyncrasy: MaxEnt versus Hierarchical Regression

Experimental research has uncovered language learners’ ability to frequency-match to statistical generalizations across the lexicon, while also acquiring the idiosyncratic behavior of individual attested words. How can we model the learning of a frequency-matching grammar together with lexical idiosyncrasy? A recent approach based in the single-level regression model Maximum Entropy Harmonic Grammar makes use of general constraints that putatively capture statistical generalizations across the lexicon, as well as lexical constraints governing the behavior of individual words. I argue on the basis of learning simulations that the approach fails to learn statistical generalizations across the lexicon, running into what I call the GRAMMAR-LEXICON BALANCING PROBLEM: lexical constraints are so powerful that the learner comes to acquire the behavior of each attested form using only these constraints, at which point the general constraint is rendered superfluous and ineffective. I argue that MaxEnt be replaced with the HIERARCHICAL REGRESSION MODEL: multiple layers of regression structure, corresponding to different levels of a hierarchy of generalizations. Hierarchical regression is shown to surmount the grammar-lexicon balancing problem—learning a frequency-matching grammar together with lexical idiosyncrasy—by encoding general constraints as fixed effects and lexical constraints as a random effect. The model is applied to variable Slovenian palatalization, with promising results.