Natural language generators (NLGs) for taskoriented dialogue typically take a meaning representation (MR) as input, and are trained endto-end with a corpus of MR/utterance pairs, where the MRs cover a specific set of dialogue acts and domain attributes.Creation of such datasets is labor intensive and time consuming.Therefore, dialogue systems for new domain ontologies would benefit from using data for pre-existing ontologies.Here we explore, for the first time, whether it is possible to train an NLG for a new larger ontology using existing training sets for the restaurant domain, where each set is based on a different ontology.We create a new, larger combined ontology, and then train an NLG to produce utterances covering it.For example, if one dataset has attributes for family friendly and rating information, and the other has attributes for decor and service, our aim is an NLG for the combined ontology that can produce utterances that realize values for family friendly, rating, decor and service.Initial experiments with a baseline neural sequence-to-sequence model show that this task is surprisingly challenging.We then develop a novel self-training method that identifies (errorful) model outputs, automatically constructs a corrected MR input to form a new (MR, utterance) training pair, and then repeatedly adds these new instances back into the training data.We then test the resulting model on a new test set.The result is a selftrained model whose performance is an absolute 75.4% improvement over the baseline model.We also report a human qualitative evaluation of the final model showing that it achieves high naturalness, semantic coherence and grammaticality.

Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive.This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output.We present YELPNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains.The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics.

Question Generation is the task of automatically creating questions from textual input.In this work we present a new Attentional Encoder-Decoder Recurrent Neural Network model for automatic question generation.Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels.The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution.In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence.Our model achieves state of the art results of 19.98 Bleu 4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin.A human evaluation also shows that the added features improve the quality of the generated questions.

Effective models of social dialog must understand a broad range of rhetorical and figurative devices.Rhetorical questions (RQs) are a type of figurative language whose aim is to achieve a pragmatic goal, such as structuring an argument, being persuasive, emphasizing a point, or being ironic.While there are computational models for other forms of figurative language, rhetorical questions have received little attention to date.We expand a small dataset from previous work, presenting a corpus of 10,270 RQs from debate forums and Twitter that represent different discourse functions.We show that we can clearly distinguish between RQs and sincere questions (0.76 F1).We then show that RQs can be used both sarcastically and non-sarcastically, observing that non-sarcastic (other) uses of RQs are frequently argumentative in forums, and persuasive in tweets.We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0.76 F1 for SARCASTIC and 0.77 F1 for OTHER in forums, and 0.83 F1 for both SARCASTIC and OTHER in tweets.We supplement our quantitative experiments with an in-depth characterization of the linguistic variation in RQs.

The use of irony and sarcasm in social media allows us to study them at scale for the first time.However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue.Here, we describe the process of creating a largescale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole.We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy.To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue.We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class.