Parsing the Impossible, Translating the Improbable – PARSITI

Social media and other forms of online communication have triggered
the emergence of new forms of written texts and increased the volume of
multilingual user-generated content (UGC). Making these unlimited streams of
non-canonical texts automatically understandable and actionable opens
new scientific and social challenges. This is the main focus of the
ParSiTi project.

One of the most striking influences of social media on society is how
they evolved to impact our perception of events. For instance, during
the various Spring Revolutions, Facebook users were in the front line
of the information war; more recently, during the November 2015 Paris
Attacks, Twitter was used to gather information about the victims and
to offer shelter to those stranded by these attacks. These events
generated a steady flow of global textual interactions, crucially
highlighting the lack of accurate tools to automatically process and
understand these information streams.

UGC, covering among others social networks, blogs and forums, differ
from newspaper written languages, on which natural language processing
(NLP) tools are most often trained and tested, in three important
dimensions: (i) user-generated content is extremely diverse,
rife with abbreviations, spelling mistakes, typographical and
grammatical errors. It often lacks punctuation and mixes languages. In
some cases, the spelling is akin to rough phonetisation. Added to a
much richer variability, these phenomena hinder the performance of NLP
pipelines. (ii) Overcoming English, the Web has now turned into a
truly multilingual space. (iii) A strong
contextualization as these non-canonical productions are tightly
linked to contextual sources (videos, images, memes, game sessions,
external URLs) and the inner nature of most social media encourages
shorter sentences and threaded messages, which in turn favor the use
of elliptical constructions. This leads to strong difficulties in
rising ambiguities, for example in case of underspecified anaphoras,
which complicate NLP tasks such as parsing or Machine Translation.

The ParSiTi project aims at taking advantage of recent advances
in statistical NLP and Deep Learning to address these challenges and
improve access to multilingual user-generated content. We plan to
design and release a fully integrated NLP software able to
automatically process non-canonical texts in their
context. To demonstrate the success of our approach, an accurate
Machine Translation system, able to translate, in context,
user-generated content between French, Arabic and English, will be
developed. Such a system should prove valuable to researchers in
linguistics, social sciences and for innovative private sector
companies. Moreover, our software and data sets will be made freely
available, so that they can be used for further work beyond the scope
of the project, e.g. for information extraction or opinion mining.

Developing this software is higly challenging and requires to push
existing techniques to their limits, sometimes at the price of
questioning assumptions that have long been taken for granted. The
ParSiTi project will address three scientific challenges of
increasing risk and complexity: (i) normalizing UGC and adapting
parsing, along with translation models, to their peculiarities (ii) developing
joint models to combine different sources of information without error
propagation (iii) design context-aware models to cope with discussion
anchored in a specific linguistic (e.g. comment in a threaded
discussion) and extract-linguistic (e.g. images, URL, ...)
contexts.

ParSiTi will gather three partners: LIMSI for their expertise in
Machine Translation and Deep Learning, LIPN for their expertise in
joint models and parsing, and ALPAGE for their expertise in
morpho-syntactic processing of social media, (deep) parsing, and out-of-domain
adaptation.