Search Results for author:
Found 18 papers, 5 papers with code
Automatically Exposing Problems with Neural Dialog Models
Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses.
Language Embeddings for Typology and Cross-lingual Transfer Learning
Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
+4
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models
Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities.
Tracking the Evolution of Written Language Competence in L2 Spanish Learners
In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students{'} written productions.
Developing NLP Tools with a New Corpus of Learner Spanish
While annotated learner corpora of English are widely available, large learner corpora of Spanish are less common.
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder
We present an encoder-decoder model for semantic parsing with UCCA SemEval 2019 Task 1.
Studying the Difference Between Natural and Programming Language Corpora
Code corpora, as observed in large software systems, are now known to be far more repetitive and predictable than natural language corpora.
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States
Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art.
Fast Rhetorical Structure Theory Discourse Parsing
In recent years, There has been a variety of research on discourse parsing, particularly RST discourse parsing.
Practical Evaluation of Human and Synthesized Speech for Virtual Human Dialogue Systems
To determine the best trade-off between performance and cost, we perform a systematic evaluation of human and synthesized voices with regard to naturalness, conversational aspect, and likability.
