This paper shows that discriminative reranking with an averaged perceptron model yields substantial improvements in realization quality with CCG. The paper confirms the utility of including language model log probabilities as features in the model, which prior work on discriminative training with log linear models for HPSG realization had called into question. The perceptron model allows the combination of multiple n-gram models to be optimized and then augmented with both syntactic features and discriminative n-gram features. The full model yields a stateof-the-art BLEU score of 0.8506 on Section 23 of the CCGbank, to our knowledge the best score reported to date using a reversible, corpus-engineered grammar.

We investigate the extent to which syntactic choice in written English is influenced by processing considerations as predicted by Gibson's (2000) Dependency Locality Theory (DLT) and Surprisal Theory (Hale, 2001; Levy, 2008). A long line of previous work attests that languages display a tendency for shorter dependencies, and in a previous corpus study, Temperley (2007) provided evidence that this tendency exerts a strong influence on constituent ordering choices. However, Temperley's study included no frequency-based controls, and subsequent work on sentence comprehension with broad-coverage eye-tracking corpora found weak or negative effects of DLT-based measures when frequency effects were statistically controlled for (Demberg & Keller, 2008; van Schijndel, Nguyen, & Schuler 2013; van Schijndel & Schuler, 2013), calling into question the actual impact of dependency locality on syntactic choice phenomena. Going beyond Temperley's work, we show that DLT integration costs are indeed a significant predictor of syntactic choice in written English even in the presence of competing frequency-based and cognitively motivated control factors, including n-gram probability and PCFG surprisal as well as embedding depth (Wu, Bachrach, Cardenas, & Schuler, 2010; Yngve, 1960). Our study also shows that the predictions of dependency length and surprisal are only moderately correlated, a finding which mirrors Dember & Keller's (2008) results for sentence comprehension. Further, we demonstrate that the efficacy of dependency length in predicting the corpus choice increases with increasing head-dependent distances. At the same time, we find that the tendency towards dependency locality is not always observed, and with pre-verbal adjuncts in particular, non-locality cases are found more often than not. In contrast, surprisal is effective in these cases, and the embedding depth measures further increase prediction accuracy. We discuss the implications of our findings for theories of language comprehension and production, and conclude with a discussion of questions our work raises for future research.

This paper describes a more precise analysis of punctuation for a bi-directional, broad coverage English grammar extracted from the CCGbank (Hockenmaier and Steedman, 2007). We discuss various approaches which have been proposed in the literature to constrain overgeneration with punctuation, and illustrate how aspects of Briscoe's (1994) influential approach, which relies on syntactic features to constrain the appearance of balanced and unbalanced commas and dashes to appropriate sentential contexts, is unattractive for CCG. As an interim solution to constrain overgeneration, we propose a rule-based filter which bars illicit sequences of punctuation and cases of improperly unbalanced apposition. Using the OpenCCG toolkit, we demonstrate that our punctuation-augmented grammar yields substantial increases in surface realization coverage and quality, helping to achieve state-of-the-art BLEU scores.

In this survey, we review recent progress on surface realization in natural language generation (NLG), highlighting how machine learning models have moved beyond n-grams to successfully incorporate linguistic insights into increasingly rich models. We also advance the view that NLG still has much to gain by taking up insights from psycholinguistic studies -not only of human production but also of comprehension. We highlight how realization ranking models can be improved by modeling the role of memory in human language comprehension and discuss how surface realizers might transition to using grammars developed for incremental parsing in computational psycholinguistics, thereby making them more suitable for integration into real-time incremental dialog systems. From a production standpoint, we suggest that the principle of UNIFORM INFORMATION DENSITY has the potential to enhance the theoretical basis for choice making in NLG and discuss two initial steps in this direction. Finally, we conclude our survey with a discussion of prospects for community-based evaluation of surface realization systems.

Based on the Production-Distribution-Comprehension (PDC) account of language processing, we formulate two distinct hypotheses about case marking, word order choices and processing in Hindi. Our first hypothesis is that Hindi tends to optimize for processing efficiency at both lexical and syntactic levels. We quantify the role of case markers in this process. For the task of predicting the reference sentence occurring in a corpus (amidst meaning-equivalent grammatical variants) using a machine learning model, surprisal estimates from an artificial version of the language (i.e., Hindi without any case markers) result in lower prediction accuracy compared to natural Hindi. Our second hypothesis is that Hindi tends to minimize interference due to case markers while ordering preverbal constituents. We show that Hindi tends to avoid placing next to each other constituents whose heads are marked by identical case inflections. Our findings adhere to PDC assumptions and we discuss their implications for language production, learning and universals.

This study examines the role of three influential theories of language processing, viz., Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.

This paper describes how named entity (NE) classes can be used to improve broad coverage surface realization with the OpenCCG realizer. Our experiments indicate that collapsing certain multi-word NEs and interpolating a language model where NEs are replaced by their class labels yields the largest quality increase, with 4-grams adding a small additional boost. Substantial further benefit is obtained by including class information in the hypertagging (supertagging for realization) component of the system, yielding a state-of-theart BLEU score of 0.8173 on Section 23 of the CCGbank. A targeted manual evaluation confirms that the BLEU score increase corresponds to a significant rise in fluency.

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