The long-standing one-to-many issue of the open-domain dialogues poses
s...
Much recent effort has been devoted to creating large-scale language mod...
We compare sequential fine-tuning with a model for multi-task learning i...
Deep neural models, in particular Transformer-based pre-trained language...
This paper presents the shared task on Multilingual Idiomaticity Detecti...
Tokenisation is the first step in almost all NLP tasks, and state-of-the...
Despite their success in a variety of NLP tasks, pre-trained language mo...
When documenting oral-languages, Unsupervised Word Segmentation (UWS) fr...
For endangered languages, data collection campaigns have to accommodate ...
For language documentation initiatives, transcription is an expensive
re...
In distributional semantics, the pointwise mutual information
(PMI) weig...
Since Bahdanau et al. [1] first introduced attention for neural machine
...
This paper presents an extension to a very low-resource parallel corpus
...
We present a first attempt to perform attentional word segmentation dire...
The positive effect of adding subword information to word embeddings has...
Word discovery is the task of extracting words from unsegmented text. In...
Increasing the capacity of recurrent neural networks (RNN) usually invol...
In this paper we take a state-of-the-art model for distributed word
repr...
In this paper, we propose LexVec, a new method for generating distribute...