4 papers
Pantagruel: Unified Self-Supervised Encoders for French Text and Speech
Phuong-Hang Le, Valentin Pelloin, Arnault Chatelain +27
We release Pantagruel models, a new family of self-supervised encoder models for French text and speech. Instead of predicting modality-tailored targets such as textual tokens or s…
ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
Jeongwoo Kang, Maria Boritchev, Maximin Coavoux
We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR). Specifically, we annotate the DinG corpus, consisting…
Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding
Jeongwoo Kang, Maximin Coavoux, Cédric Lopez +1
Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al., 2013, AMR) parsers. To train such models, AMR graphs have to be linearized i…
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing
Jeongwoo Kang, Maximin Coavoux, Cédric Lopez +1
Cross-lingual AMR parsing is the task of predicting AMR graphs in a target language when training data is available only in a source language. Due to the small size of AMR training…