Neural models have drastically advanced state of the art for machine
tra...
In recent years machine translation has become very successful for
high-...
With a growing focus on morphological inflection systems for languages w...
Large multilingual models have inspired a new class of word alignment
me...
Many dialogue systems (DSs) lack characteristics humans have, such as em...
The field of natural language processing (NLP) has grown over the last f...
Neural networks have long been at the center of a debate around the cogn...
Pretrained multilingual models enable zero-shot learning even for unseen...
Morphologically-rich polysynthetic languages present a challenge for NLP...
Automatic morphological processing can aid downstream natural language
p...
Recent work has raised concerns about the inherent limitations of text-o...
We present the findings of the LoResMT 2021 shared task which focuses on...
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning...
High-performing machine translation (MT) systems can help overcome langu...
Recent advances in natural language processing (NLP) have the ability to...
We present a new probing dataset named PROST: Physical Reasoning about
O...
Pretrained multilingual models (PMMs) enable zero-shot learning via
cros...
Pretrained multilingual models are able to perform cross-lingual transfe...
Linguistically informed analyses of language models (LMs) contribute to ...
In contrast to their word- or sentence-level counterparts, character
emb...
Canonical morphological segmentation consists of dividing words into the...
We propose a new task in the area of computational creativity: acrostic ...
We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on
...
In this paper, we describe the findings of the SIGMORPHON 2020 shared ta...
Neural unsupervised parsing (UP) models learn to parse without access to...
Intermediate-task training has been shown to substantially improve pretr...
In this paper, we present the systems of the University of Stuttgart IMS...
We propose the task of unsupervised morphological paradigm completion. G...
While pretrained models such as BERT have shown large gains across natur...
Part-of-speech (POS) taggers for low-resource languages which are exclus...
We propose to cast the task of morphological inflection - mapping a lemm...
The relation between language and thought has occupied linguists for at ...
How does knowledge of one language's morphology influence learning of
in...
Development sets are impractical to obtain for real low-resource languag...
Multi-task learning and self-training are two common ways to improve a
m...
Word embeddings typically represent different meanings of a word in a si...
Language identification for code-switching (CS), the phenomenon of
alter...
Verbs occur in different syntactic environments, or frames. We investiga...
The CoNLL--SIGMORPHON 2018 shared task on supervised learning of
morphol...
Neural state-of-the-art sequence-to-sequence (seq2seq) models often do n...
Motivated by recent findings on the probabilistic modeling of acceptabil...
Embedding models typically associate each word with a single real-valued...
Machine translation from polysynthetic to fusional languages is a challe...
Morphological segmentation for polysynthetic languages is challenging,
b...
We present a semi-supervised way of training a character-based
encoder-d...
We present a novel cross-lingual transfer method for paradigm completion...
Deep neural networks (DNN) have revolutionized the field of natural lang...
We explore the task of multi-source morphological reinflection, which
ge...
Morphological reinflection is the task of generating a target form given...