most citedTraining Neural Response Selection for Task-Oriented Dialogue Systems

55 citations · 105 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL201955 cited

Training Neural Response Selection for Task-Oriented Dialogue Systems

Matthew Henderson, Ivan Vulić, Daniela Gerz +7

Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application bei…

cs.CL20199 cited

A Repository of Conversational Datasets

Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva +8

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a r…

cs.CL2019

A Systematic Study of Leveraging Subword Information for Learning Word Representations

Yi Zhu, Ivan Vulić, Anna Korhonen

The use of subword-level information (e.g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning. Its importance is attested espec…

cs.CL2017

Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

Ivan Vulić, Nikola Mrkšić, Anna Korhonen

Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In thi…

cs.CL20173 cited

Decoding Sentiment from Distributed Representations of Sentences

Edoardo Maria Ponti, Ivan Vulić, Anna Korhonen

Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such represen…

cs.CL2017

Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules

Ivan Vulić, Nikola Mrkšić, Roi Reichart +3

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms;…