5,225 research outputs found
What to do about non-standard (or non-canonical) language in NLP
Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered standard, most prominently English newswire. However, there are
many dimensions, e.g., socio-demographics, language, genre, sentence type, etc.
on which texts can differ from the standard. The solution is not obvious: we
cannot control for all factors, and it is not clear how to best go beyond the
current practice of training on homogeneous data from a single domain and
language.
In this paper, I review the notion of canonicity, and how it shapes our
community's approach to language. I argue for leveraging what I call fortuitous
data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight,
or raw data that needs to be refined. If we embrace the variety of this
heterogeneous data by combining it with proper algorithms, we will not only
produce more robust models, but will also enable adaptive language technology
capable of addressing natural language variation.Comment: KONVENS 201
One model, two languages: training bilingual parsers with harmonized treebanks
We introduce an approach to train lexicalized parsers using bilingual corpora
obtained by merging harmonized treebanks of different languages, producing
parsers that can analyze sentences in either of the learned languages, or even
sentences that mix both. We test the approach on the Universal Dependency
Treebanks, training with MaltParser and MaltOptimizer. The results show that
these bilingual parsers are more than competitive, as most combinations not
only preserve accuracy, but some even achieve significant improvements over the
corresponding monolingual parsers. Preliminary experiments also show the
approach to be promising on texts with code-switching and when more languages
are added.Comment: 7 pages, 4 tables, 1 figur
NLP and ML Methods for Pre-processing, Clustering and Classification of Technical Logbook Datasets
Technical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP pipelines. These datasets typically represent a domain (a technical field such as automotive) and an application (e.g., maintenance). The granularity of issue types described in these datasets additionally leads to class imbalance, making it challenging for models to accurately predict which issue each logbook entry describes. In this research, we focus on the problem of technical issue pre-processing, clustering, and classification by considering logbook datasets from the automotive, aviation, and facility maintenance domains. We developed MaintNet, a collaborative open source library including logbook datasets from various domains and a pre-processing pipeline to clean unstructured datasets. Additionally, we adapted a feedback loop strategy from computer vision for handling extreme class imbalance, which resamples the training data based on its error in the prediction process. We further investigated the benefits of using transfer learning from sources within the same domain (but different applications), from within the same application (but different domains), and from all available data to improve the performance of the classification models. Finally, we evaluated several data augmentation approaches including synonym replacement, random swap, and random deletion to address the issue of data scarcity in technical logbooks
Automatic case acquisition from texts for process-oriented case-based reasoning
This paper introduces a method for the automatic acquisition of a rich case
representation from free text for process-oriented case-based reasoning. Case
engineering is among the most complicated and costly tasks in implementing a
case-based reasoning system. This is especially so for process-oriented
case-based reasoning, where more expressive case representations are generally
used and, in our opinion, actually required for satisfactory case adaptation.
In this context, the ability to acquire cases automatically from procedural
texts is a major step forward in order to reason on processes. We therefore
detail a methodology that makes case acquisition from processes described as
free text possible, with special attention given to assembly instruction texts.
This methodology extends the techniques we used to extract actions from cooking
recipes. We argue that techniques taken from natural language processing are
required for this task, and that they give satisfactory results. An evaluation
based on our implemented prototype extracting workflows from recipe texts is
provided.Comment: Sous presse, publication pr\'evue en 201
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Pretrained language models have become the standard approach for many NLP
tasks due to strong performance, but they are very expensive to train. We
propose a simple and efficient learning framework, TLM, that does not rely on
large-scale pretraining. Given some labeled task data and a large general
corpus, TLM uses task data as queries to retrieve a tiny subset of the general
corpus and jointly optimizes the task objective and the language modeling
objective from scratch. On eight classification datasets in four domains, TLM
achieves results better than or similar to pretrained language models (e.g.,
RoBERTa-Large) while reducing the training FLOPs by two orders of magnitude.
With high accuracy and efficiency, we hope TLM will contribute to democratizing
NLP and expediting its development.Comment: 14 pages, 5 figure
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