Yanran Li

Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering

2017

Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional …

Incorporating Relevant Knowledge in Context Modeling and Response Generation

2018 · arXiv (Cornell University)

To sustain engaging conversation, it is critical for chatbots to make good use of relevant knowledge. Equipped with a knowledge base, chatbots are able to extract conversation-related attributes and entities to facilitate context modeling and …

Component-Enhanced Chinese Character Embeddings

2015 · arXiv (Cornell University)

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding …

DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

2017 · arXiv (Cornell University)

We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics …