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Found 26 papers, 4 papers with code
Unsupervised Melody-to-Lyric Generation
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody.
Unsupervised Melody-Guided Lyrics Generation
At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
ExPUNations: Augmenting Puns with Keywords and Explanations
The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master.
Context-Situated Pun Generation
In this work, we propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words.
Style Control for Schema-Guided Natural Language Generation
Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods.
Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation
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.
Schema-Guided Natural Language Generation
We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity.
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast
Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant.
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.
Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?
While neural generation methods integrate sentence planning and surface realization in one end-to-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training.
Neural MultiVoice Models for Expressing Novel Personalities in Dialog
Natural language generators for task-oriented dialog should be able to vary the style of the output utterance while still effectively realizing the system dialog actions and their associated semantics.
Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators
We show that our most explicit model can simultaneously achieve high fidelity to both semantic and stylistic goals: this model adds a context vector of 36 stylistic parameters as input to the hidden state of the encoder at each time step, showing the benefits of explicit stylistic supervision, even when the amount of training data is large.
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems
In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open domain interaction where we cannot rely on domain-specific inference.
Exploring Conversational Language Generation for Rich Content about Hotels
Dialogue systems for hotel and tourist information have typically simplified the richness of the domain, focusing system utterances on only a few selected attributes such as price, location and type of rooms.
TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation
In the area of natural language generation (NLG), there has been a great deal of interest in end-to-end (E2E) neural models that learn and generate natural language sentence realizations in one step.
Ranked #7 on
Data-to-Text Generation
on E2E NLG Challenge
(using extra training data)
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework
In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics.
Summarizing Dialogic Arguments from Social Media
We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0. 74 for gun control, 0. 71 for gay marriage, and 0. 67 for abortion.
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog
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.
And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue
We investigate the characteristics of factual and emotional argumentation styles observed in online debates.
Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews
We learn hyperbolic adjective patterns that are representative of the strongly-valenced expressive language commonly used in either positive or negative reviews.
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
The use of irony and sarcasm in social media allows us to study them at scale for the first time.
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries.
Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
The greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do not follow a particular plan or pursue a particular fixed information need.
Data-Driven Dialogue Systems for Social Agents
In order to build dialogue systems to tackle the ambitious task of holding social conversations, we argue that we need a data driven approach that includes insight into human conversational chit chat, and which incorporates different natural language processing modules.
Learning Lexico-Functional Patterns for First-Person Affect
Informal first-person narratives are a unique resource for computational models of everyday events and people's affective reactions to them.
