65 research outputs found
An Analysis of Source-Side Grammatical Errors in NMT
The quality of Neural Machine Translation (NMT) has been shown to
significantly degrade when confronted with source-side noise. We present the
first large-scale study of state-of-the-art English-to-German NMT on real
grammatical noise, by evaluating on several Grammar Correction corpora. We
present methods for evaluating NMT robustness without true references, and we
use them for extensive analysis of the effects that different grammatical
errors have on the NMT output. We also introduce a technique for visualizing
the divergence distribution caused by a source-side error, which allows for
additional insights.Comment: Accepted and to be presented at BlackboxNLP 201
Pushing the Limits of Low-Resource Morphological Inflection
Recent years have seen exceptional strides in the task of automatic
morphological inflection generation. However, for a long tail of languages the
necessary resources are hard to come by, and state-of-the-art neural methods
that work well under higher resource settings perform poorly in the face of a
paucity of data. In response, we propose a battery of improvements that greatly
improve performance under such low-resource conditions. First, we present a
novel two-step attention architecture for the inflection decoder. In addition,
we investigate the effects of cross-lingual transfer from single and multiple
languages, as well as monolingual data hallucination. The macro-averaged
accuracy of our models outperforms the state-of-the-art by 15 percentage
points. Also, we identify the crucial factors for success with cross-lingual
transfer for morphological inflection: typological similarity and a common
representation across languages.Comment: to appear at EMNLP 201
Phylogeny-Inspired Adaptation of Multilingual Models to New Languages
Large pretrained multilingual models, trained on dozens of languages, have
delivered promising results due to cross-lingual learning capabilities on
variety of language tasks. Further adapting these models to specific languages,
especially ones unseen during pre-training, is an important goal towards
expanding the coverage of language technologies. In this study, we show how we
can use language phylogenetic information to improve cross-lingual transfer
leveraging closely related languages in a structured, linguistically-informed
manner. We perform adapter-based training on languages from diverse language
families (Germanic, Uralic, Tupian, Uto-Aztecan) and evaluate on both syntactic
and semantic tasks, obtaining more than 20% relative performance improvements
over strong commonly used baselines, especially on languages unseen during
pre-training.Comment: accepted in AACL 2022 Main Conferenc
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