268 citations · 662 across the 69 of their papers we have counts for
11 papers · 2 filters
ConveRT: Efficient and Accurate Conversational Representations from Transformers
Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić +3
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.…
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Yi Zhu, Benjamin Heinzerling, Ivan Vulić +3
Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value sh…
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?
Ivan Vulić, Goran Glavaš, Roi Reichart +1
Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-l…
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
Matthew Henderson, Ivan Vulić, Iñigo Casanueva +7
We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of t…
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Anne Lauscher, Goran Glavaš, Simone Paolo Ponzetto +1
Distributional word vectors have recently been shown to encode many of the human biases, most notably gender and racial biases, and models for attenuating such biases have conseque…
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
Anne Lauscher, Ivan Vulić, Edoardo Maria Ponti +2
Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional stat…
Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems
Paweł Budzianowski, Ivan Vulić
Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are e…
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…
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…
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…
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
Goran Glavas, Robert Litschko, Sebastian Ruder +1
Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream tasks…