Generating expository dialogue from monologue: Motivation, corpus and preliminary rules

Generating expository dialogue from monologue is a task that poses an interesting and rewarding challenge for Natural Language Processing. This short paper has three aims: firstly, to motivate the importance of this task, both in terms of the benefits of expository dialogue as a way to present information and in terms of potential applications; secondly, to introduce a parallel corpus of monologues and dialogues which enables a data-driven approach to this challenge; and, finally, to describe work-in-progress on semi-automatic construction of Monologueto-Dialogue (M2D) generation rules

Fine-Grained Control of Sentence Segmentation and Entity Positioning in Neural NLG

International audienceThe move from pipeline Natural Language Generation (NLG) approaches to neural end-to-end approaches led to a loss of control in sentence planning operations owing to the conflation of intermediary micro-planning stages into a single model. Such control is highly necessary when the text should be tailored to respect some constraints such as which entity to be mentioned first, the entity position, the complexity of sentences, etc. In this paper, we introduce fine-grained control of sentence planning in neural data-to-text generation models at two levels-realization of input entities in desired sentences and realization of the input entities in the desired position among individual sentences. We show that by augmenting the input with explicit position identi-fiers, the neural model can achieve a great control over the output structure while keeping the naturalness of the generated text intact. Since sentence level metrics are not entirely suitable to evaluate this task, we used a metric specific to our task that accounts for the model's ability to achieve control. The results demonstrate that the position identifiers do constraint the neural model to respect the intended output structure which can be useful in a variety of domains that require the generated text to be in a certain structure

Natural language response generation in mixed-initiative dialogs.

Yip Wing Lin Winnie.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 102-105).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Thesis Goals --- p.3Chapter 1.3 --- Thesis Outline --- p.5Chapter 2 --- Background --- p.6Chapter 2.1 --- Natural Language Generation --- p.6Chapter 2.1.1 --- Template-based Approach --- p.7Chapter 2.1.2 --- Rule-based Approach --- p.8Chapter 2.1.3 --- Statistical Approach --- p.9Chapter 2.1.4 --- Hybrid Approach --- p.10Chapter 2.1.5 --- Machine Learning Approach --- p.11Chapter 2.2 --- Evaluation Method --- p.12Chapter 2.2.1 --- Cooperative Principles --- p.13Chapter 2.3 --- Chapter Summary --- p.13Chapter 3 --- Natural Language Understanding --- p.14Chapter 3.1 --- The CUHK Restaurant Domain --- p.15Chapter 3.2 --- "Task Goals, Dialog Acts, Concept Categories and Annotation" --- p.17Chapter 3.2.1 --- Task Goals (TGs) and Dialog Acts (DAs) --- p.17Chapter 3.2.2 --- Concept Categories (CTG/CDA) --- p.20Chapter 3.2.3 --- Utterance Segmentation and Annotation --- p.21Chapter 3.3 --- Task Goal and Dialog Act Identification --- p.22Chapter 3.3.1 --- Belief Networks Development --- p.22Chapter 3.3.2 --- Task Goal and Dialog Act Inference --- p.24Chapter 3.3.3 --- Network Dimensions --- p.25Chapter 3.4 --- Chapter Summary --- p.29Chapter 4 --- Automatic Utterance Segmentation --- p.30Chapter 4.1 --- Utterance Definition --- p.31Chapter 4.2 --- Segmentation Procedure --- p.33Chapter 4.2.1 --- Tokenization --- p.35Chapter 4.2.2 --- POS Tagging --- p.36Chapter 4.2.3 --- Multi-Parser Architecture (MPA) Language Parsing --- p.38Chapter 4.2.4 --- Top-down Generalized Representation --- p.40Chapter 4.3 --- Evaluation --- p.47Chapter 4.3.1 --- Results --- p.47Chapter 4.3.2 --- Analysis --- p.48Chapter 4.4 --- Chapter Summary --- p.50Chapter 5 --- Natural Language Response Generation --- p.52Chapter 5.1 --- System Overview --- p.52Chapter 5.2 --- Corpus-derived Dialog State Transition Rules --- p.55Chapter 5.3 --- Hand-designed Text Generation Templates --- p.56Chapter 5.4 --- Performance Evaluation --- p.59Chapter 5.4.1 --- Task Completion Rate --- p.61Chapter 5.4.2 --- Grice's Maxims and Perceived User Satisfaction --- p.62Chapter 5.4.3 --- Error Analysis --- p.64Chapter 5.5 --- Chapter Summary --- p.65Chapter 6 --- Bilingual Response Generation using Semi-Automatically- Induced Response Templates --- p.67Chapter 6.1 --- Response Data --- p.68Chapter 6.2 --- Semi-Automatic Grammar Induction --- p.69Chapter 6.2.1 --- Agglomerative Clustering --- p.69Chapter 6.2.2 --- Parameters Selection --- p.70Chapter 6.3 --- Application to Response Grammar Induction --- p.71Chapter 6.3.1 --- Parameters Selection --- p.73Chapter 6.3.2 --- Unsupervised Grammar Induction --- p.76Chapter 6.3.3 --- Post-processing --- p.80Chapter 6.3.4 --- Prior Knowledge Injection --- p.82Chapter 6.4 --- Response Templates Generation --- p.84Chapter 6.4.1 --- Induced Response Grammar --- p.84Chapter 6.4.2 --- Template Formation --- p.84Chapter 6.4.3 --- Bilingual Response Templates --- p.89Chapter 6.5 --- Evaluation --- p.89Chapter 6.5.1 --- "Task Completion Rate, Grice's Maxims and User Sat- isfaction" --- p.91Chapter 6.6 --- Chapter Summary --- p.94Chapter 7 --- Conclusion --- p.96Chapter 7.1 --- Summary --- p.96Chapter 7.2 --- Contributions --- p.98Chapter 7.3 --- Future Work --- p.100Bibliography --- p.102Chapter A --- Domain-Specific Task Goals in the CUHK Restaurants Do- main --- p.107Chapter B --- Full List of VERBMOBIL-2 Dialog Acts --- p.109Chapter C --- Dialog Acts for Customer Requests and Waiter Responsesin the CUHK Restaurants Domain --- p.111Chapter D --- Grammar for Task Goal and Dialog Act Identification --- p.116Chapter E --- Utterance Definition --- p.119Chapter F --- Dialog State Transition Rules --- p.121Chapter G --- Full List of Templates Selection Conditions --- p.125Chapter H --- Hand-designed Text Generation Templates --- p.130Chapter I --- Evaluation Test Questionnaire for Dialog System in the CUHK Restaurant Domain --- p.135Chapter J --- POS Tags --- p.137Chapter K --- Full List of Lexicon and contextual rule modifications --- p.139Chapter L --- Top-down Generalized Representations --- p.141Chapter M --- Sample Outputs for Automatic Utterance Segmentation --- p.144Chapter N --- Induced Grammar --- p.145Chapter O --- Seeded Categories --- p.148Chapter P --- Semi-Automatically-Induced Response Templates --- p.150Chapter Q --- Details of the Statistical Testing Regarding Grice's Maxims and User Satisfaction --- p.15

Towards collaborative dialogue in Minecraft

This dissertation describes our work in building interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. To investigate the challenges of building such agents, we define a novel instantiation of a situated, Minecraft-based, Collaborative Building Task in which one player (A, the Architect) is shown a target structure, denoted Target, and needs to instruct the other player (B, the Builder) to build a copy of this structure, denoted Built, in a predefined build region. While both players can interact asynchronously via a chat interface, we define the roles to be asymmetric: A can observe B and Target, but is invisible and cannot place blocks; meanwhile, B can freely place and remove blocks, but has no explicit knowledge of the target structure. Each agent requires a different set of abilities in order to be successful at this task: specifically, A's main challenges arise in the task of generating situated instructions by comparing Built and Target, while B's responsibilities focus mainly on comprehending A's situated instructions using both dialogue and world context. Both agents must be able to interact asynchronously in an evolving dialogue context and a dynamic world state within which they are embodied. In this work, we specifically examine how well end-to-end neural models can learn to be instruction givers (i.e., Architects) from a limited amount of real human-human data. In order to examine how humans complete the Collaborative Building Task, as well as use human-human data as a gold standard for training and evaluating models, we present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. We then introduce baseline models for the challenging subtask of Architect utterance generation, and evaluate them offline, using both automated metrics and human evaluation. We show that while conditioning our model on a simple representation of the world gives our model improved ability to generate correct instructions, there are still many obvious shortcomings, and it is difficult for these models to learn the large variety of abilities needed to be successful Architects in an entirely end-to-end manner. To combat this, we show that including meaningful, structured inputs about the world and discourse state as additional inputs -- specifically, by adding oracle information about the Builder's next actions, as well as enriching our linguistic representation with Architect dialogue acts -- improves the performance of our utterance generation models. We also augment the data with shape information by pretraining 3D shape localization models on synthetically generated block configurations. Finally, we integrate Architect utterance generation models into actual Minecraft agents and evaluate them in a fully interactive setting