760 research outputs found
One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis
When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native language is Norwegian and you decide to learn Dutch, the lexical overlap
between these two languages will likely benefit your rate of language
acquisition. This thesis deals with the intersection of learning multiple tasks
and learning multiple languages in the context of Natural Language Processing
(NLP), which can be defined as the study of computational processing of human
language. Although these two types of learning may seem different on the
surface, we will see that they share many similarities.
The traditional approach in NLP is to consider a single task for a single
language at a time. However, recent advances allow for broadening this
approach, by considering data for multiple tasks and languages simultaneously.
This is an important approach to explore further as the key to improving the
reliability of NLP, especially for low-resource languages, is to take advantage
of all relevant data whenever possible. In doing so, the hope is that in the
long term, low-resource languages can benefit from the advances made in NLP
which are currently to a large extent reserved for high-resource languages.
This, in turn, may then have positive consequences for, e.g., language
preservation, as speakers of minority languages will have a lower degree of
pressure to using high-resource languages. In the short term, answering the
specific research questions posed should be of use to NLP researchers working
towards the same goal.Comment: PhD thesis, University of Groninge
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural
language processing (NLP). Yet, what `good generalisation' entails and how it
should be evaluated is not well understood, nor are there any common standards
to evaluate it. In this paper, we aim to lay the ground-work to improve both of
these issues. We present a taxonomy for characterising and understanding
generalisation research in NLP, we use that taxonomy to present a comprehensive
map of published generalisation studies, and we make recommendations for which
areas might deserve attention in the future. Our taxonomy is based on an
extensive literature review of generalisation research, and contains five axes
along which studies can differ: their main motivation, the type of
generalisation they aim to solve, the type of data shift they consider, the
source by which this data shift is obtained, and the locus of the shift within
the modelling pipeline. We use our taxonomy to classify over 400 previous
papers that test generalisation, for a total of more than 600 individual
experiments. Considering the results of this review, we present an in-depth
analysis of the current state of generalisation research in NLP, and make
recommendations for the future. Along with this paper, we release a webpage
where the results of our review can be dynamically explored, and which we
intend to up-date as new NLP generalisation studies are published. With this
work, we aim to make steps towards making state-of-the-art generalisation
testing the new status quo in NLP.Comment: 35 pages of content + 53 pages of reference
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
We introduce an architecture to learn joint multilingual sentence
representations for 93 languages, belonging to more than 30 different language
families and written in 28 different scripts. Our system uses a single BiLSTM
encoder with a shared BPE vocabulary for all languages, which is coupled with
an auxiliary decoder and trained on publicly available parallel corpora. This
enables us to learn a classifier on top of the resulting sentence embeddings
using English annotated data only, and transfer it to any of the 93 languages
without any modification. Our approach sets a new state-of-the-art on zero-shot
cross-lingual natural language inference for all the 14 languages in the XNLI
dataset but one. We also achieve very competitive results in cross-lingual
document classification (MLDoc dataset). Our sentence embeddings are also
strong at parallel corpus mining, establishing a new state-of-the-art in the
BUCC shared task for 3 of its 4 language pairs. Finally, we introduce a new
test set of aligned sentences in 122 languages based on the Tatoeba corpus, and
show that our sentence embeddings obtain strong results in multilingual
similarity search even for low-resource languages. Our PyTorch implementation,
pre-trained encoder and the multilingual test set will be freely available
Bridging the Gap in Multilingual Semantic Role Labeling: A Language-Agnostic Approach
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available. Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling. We release our model code and checkpoints at https://github.com/SapienzaNLP/multi-srl
Multilingual Language Processing From Bytes
We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads
text as bytes and outputs span annotations of the form [start, length, label]
where start positions, lengths, and labels are separate entries in our
vocabulary. Because we operate directly on unicode bytes rather than
language-specific words or characters, we can analyze text in many languages
with a single model. Due to the small vocabulary size, these multilingual
models are very compact, but produce results similar to or better than the
state-of- the-art in Part-of-Speech tagging and Named Entity Recognition that
use only the provided training datasets (no external data sources). Our models
are learning "from scratch" in that they do not rely on any elements of the
standard pipeline in Natural Language Processing (including tokenization), and
thus can run in standalone fashion on raw text
- …