23 research outputs found
Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages?
Recent work has shown that neural models can be successfully trained on multiple languages simultaneously. We investigate whether such models learn to share and exploit common syntactic knowledge among the languages on which they are trained. This extended abstract presents our preliminary result
Learning to Predict Novel Noun-Noun Compounds
We introduce temporally and contextually-aware models for the novel task of
predicting unseen but plausible concepts, as conveyed by noun-noun compounds in
a time-stamped corpus. We train compositional models on observed compounds,
more specifically the composed distributed representations of their
constituents across a time-stamped corpus, while giving it corrupted instances
(where head or modifier are replaced by a random constituent) as negative
evidence. The model captures generalisations over this data and learns what
combinations give rise to plausible compounds and which ones do not. After
training, we query the model for the plausibility of automatically generated
novel combinations and verify whether the classifications are accurate. For our
best model, we find that in around 85% of the cases, the novel compounds
generated are attested in previously unseen data. An additional estimated 5%
are plausible despite not being attested in the recent corpus, based on
judgments from independent human raters.Comment: 9 pages, 3 figures, To appear at Joint Workshop on Multiword
Expressions and WordNet (MWE-WN 2019) at ACL 2019. V3 - Fixed some typos and
updated the Data Preprocessing sectio
Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages
The scarcity of parallel data is a major limitation for Neural Machine Translation (NMT) systems, in particular for translation into morphologically rich languages (MRLs). An important way to overcome the lack of parallel data is to leverage target monolingual data, which is typically more abundant and easier to collect. We evaluate a number of techniques to achieve this, ranging from back-translation to random token masking, on the challenging task of translating English into four typologically diverse MRLs, under low-resource settings. Additionally, we introduce Inflection Pre-Training (or PT-Inflect), a novelpre-training objective whereby the NMT system is pre-trained on the task of re-inflecting lemmatized target sentences before being trained on standard source-to-target language translation. We conduct our evaluation on four typologically diverse target MRLs, and find that PT-Inflect surpasses NMT systems trained only on parallel data. While PT-Inflect is outperformed by back-translation overall, combining the two techniques leads to gains in some of the evaluated language pairs
Linguistically Motivated Subwords for English-Tamil Translation:University of Groningen’s Submission to WMT-2020
This paper describes our submission for the English-Tamil news translation task of WMT-2020. The various techniques and Neural Machine Translation (NMT) models used by our team are presented and discussed, including back-translation, fine-tuning and word dropout. Additionally, our experiments show that using a linguistically motivated subword segmentation technique (Ataman et al., 2017) does not consistently outperform the more widely used, non-linguistically motivated SentencePiece algorithm (Kudo and Richardson, 2018), despite the agglutinative nature of Tamil morphology