89 research outputs found
Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input
NLP models excel on tasks with clean inputs, but are less accurate with noisy
inputs. In particular, character-level noise such as human-written typos and
adversarially-engineered realistic-looking misspellings often appears in text
and can easily trip up NLP models. Prior solutions to address character-level
noise often alter the content of the inputs (low fidelity), thus inadvertently
lowering model accuracy on clean inputs. We proposed FiRo, an approach to boost
NLP model performance on noisy inputs without sacrificing performance on clean
inputs. FiRo sanitizes the input text while preserving its fidelity by
inferring the noise-free form for each token in the input. FiRo uses
finite-context aggregation to obtain contextual embeddings which is then used
to find the noise-free form within a restricted output space. The output space
is restricted to a small cluster of probable candidates in order to predict the
noise-free tokens more accurately. Although the clusters are small, FiRo's
effective vocabulary (union of all clusters) can be scaled up to better
preserve the input content. Experimental results show NLP models that use FiRo
outperforming baselines on six classification tasks and one sequence labeling
task at various degrees of noise.Comment: Accepted at IJCNLP-AACL 202
BERT Probe : A python package for probing attention based robustness evaluation of BERT models
Transformer models based on attention-based architectures have been significantly successful in establishing
state-of-the-art results in natural language processing (NLP). However, recent work about adversarial robustness
of attention-based models show that their robustness is susceptible to adversarial inputs causing spurious
outputs thereby raising questions about trustworthiness of such models. In this paper, we present BERT Probe
which is a python-based package for evaluating robustness to attention attribution based on character-level
and word-level evasion attacks and empirically quantifying potential vulnerabilities for sequence classification
tasks. Additionally, BERT Probe also provides two out-of-the-box defenses against character-level attention
attribution-based evasion attacks
Context-aware Adversarial Attack on Named Entity Recognition
In recent years, large pre-trained language models (PLMs) have achieved
remarkable performance on many natural language processing benchmarks. Despite
their success, prior studies have shown that PLMs are vulnerable to attacks
from adversarial examples. In this work, we focus on the named entity
recognition task and study context-aware adversarial attack methods to examine
the model's robustness. Specifically, we propose perturbing the most
informative words for recognizing entities to create adversarial examples and
investigate different candidate replacement methods to generate natural and
plausible adversarial examples. Experiments and analyses show that our methods
are more effective in deceiving the model into making wrong predictions than
strong baselines
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing
The usefulness of part-of-speech tags for parsing has been heavily questioned
due to the success of word-contextualized parsers. Yet, most studies are
limited to coarse-grained tags and high quality written content; while we know
little about their influence when it comes to models in production that face
lexical errors. We expand these setups and design an adversarial attack to
verify if the use of morphological information by parsers: (i) contributes to
error propagation or (ii) if on the other hand it can play a role to correct
mistakes that word-only neural parsers make. The results on 14 diverse UD
treebanks show that under such attacks, for transition- and graph-based models
their use contributes to degrade the performance even faster, while for the
(lower-performing) sequence labeling parsers they are helpful. We also show
that if morphological tags were utopically robust against lexical
perturbations, they would be able to correct parsing mistakes.Comment: Accepted at Findings of ACL 202
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