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Found 23 papers, 6 papers with code
WebNLG Challenge 2020: Language Agnostic Delexicalisation for Multilingual RDF-to-text generation
This paper presents our submission to the WebNLG Challenge 2020 for the English and Russian RDF-to-text generation tasks.
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG
Large pretrained models enable transfer learning to low-resource domains for language generation tasks.
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation
To build MuLAn, we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances.
Findings of the First Workshop on Simulating Conversational Intelligence in Chat
The aim of this workshop is to bring together experts working on open-domain dialogue research.
Text-to-Code Generation with Modality-relative Pre-training
Large pre-trained language models have recently been expanded and applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model--where training sequences typically contain both natural and (linearised) programming language.
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
The advent of large pre-trained language models in the domain of Code Synthesis has shown remarkable performance on various benchmarks, treating the problem of Code Generation in a fashion similar to Natural Language Generation, trained with a Language Modelling (LM) objective.
Exploring Data Augmentation for Code Generation Tasks
Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too.
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access
To alleviate the problem of structured databases' limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses.
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU
Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability.
Natural Language Understanding
Zero-Shot Cross-Lingual Transfer
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i. e. the synthesis of programming language solutions given a natural language problem description.
Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation
In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation.
Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Management
We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi-reference training and evaluation of non-deterministic agents.
Informed Sampling for Diversity in Concept-to-Text NLG
In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce.
Show Us the Way: Learning to Manage Dialog from Demonstrations
We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge.
Extracting Linguistic Resources from the Web for Concept-to-Text Generation
Many concept-to-text generation systems require domain-specific linguistic resources to produce high quality texts, but manually constructing these resources can be tedious and costly.
Generating Texts with Integer Linear Programming
Content selection, for example, may greedily select the most important facts, which may require, however, too many words to express, and this may be undesirable when space is limited or expensive.
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2).
Imitation learning for structured prediction in natural language processing
Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e. g. autonomous flight from pilot demonstrations.
Imitation learning for language generation from unaligned data
Natural language generation (NLG) is the task of generating natural language from a meaning representation.
Generating Natural Language Descriptions from OWL Ontologies: the NaturalOWL System
We present NaturalOWL, a natural language generation system that produces texts describing individuals or classes of OWL ontologies.
