14 research outputs found
Explainable NLP for Human-AI Collaboration
With more data and computing resources available these days, we have seen many novel Natural Language Processing (NLP) models breaking one performance record after another. Some of them even outperform human performance in some specific tasks. Meanwhile, many researchers have revealed weaknesses and irrationality of such models, e.g., having biases against some sub-populations, producing inconsistent predictions, and failing to work effectively in the wild due to overfitting. Therefore, in real applications, especially in high-stakes domains, humans cannot rely carelessly on predictions of NLP models, but they need to work closely with the models to ensure that every final decision made is accurate and benevolent.
In this thesis, we devise and utilize explainable NLP techniques to support human-AI collaboration using text classification as a target task. Overall, our contributions can be divided into three main parts. First, we study how useful explanations are for humans according to three different purposes: revealing model behavior, justifying model predictions, and helping humans investigate uncertain predictions. Second, we propose a framework that enables humans to debug simple deep text classifiers informed by model explanations. Third, leveraging on computational argumentation, we develop a novel local explanation method for pattern-based logistic regression models that align better with human judgements and effectively assist humans to perform an unfamiliar task in real-time. Altogether, our contributions are paving the way towards the synergy of profound knowledge of human users and the tireless power of AI machines.Open Acces
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Insufficient or even unavailable training data of emerging classes is a big
challenge of many classification tasks, including text classification.
Recognising text documents of classes that have never been seen in the learning
stage, so-called zero-shot text classification, is therefore difficult and only
limited previous works tackled this problem. In this paper, we propose a
two-phase framework together with data augmentation and feature augmentation to
solve this problem. Four kinds of semantic knowledge (word embeddings, class
descriptions, class hierarchy, and a general knowledge graph) are incorporated
into the proposed framework to deal with instances of unseen classes
effectively. Experimental results show that each and the combination of the two
phases achieve the best overall accuracy compared with baselines and recent
approaches in classifying real-world texts under the zero-shot scenario.Comment: Accepted NAACL-HLT 201
Label-Aware Automatic Verbalizer for Few-Shot Text Classification
Prompt-based learning has shown its effectiveness in few-shot text
classification. One important factor in its success is a verbalizer, which
translates output from a language model into a predicted class. Notably, the
simplest and widely acknowledged verbalizer employs manual labels to represent
the classes. However, manual selection does not guarantee the optimality of the
selected words when conditioned on the chosen language model. Therefore, we
propose Label-Aware Automatic Verbalizer (LAAV), effectively augmenting the
manual labels to achieve better few-shot classification results. Specifically,
we use the manual labels along with the conjunction "and" to induce the model
to generate more effective words for the verbalizer. The experimental results
on five datasets across five languages demonstrate that LAAV significantly
outperforms existing verbalizers. Furthermore, our analysis reveals that LAAV
suggests more relevant words compared to similar approaches, especially in
mid-to-low resource languages
Knowledge-driven slot constraints for goal-oriented dialogue systems
In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of "cheese pizza" (a menu item) and "oreo cookies" (a topping) from an input utterance "Can I order a cheese pizza with oreo cookies on top?" exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements
Towards Explainable Evaluation Metrics for Machine Translation
Unlike classical lexical overlap metrics such as BLEU, most current
evaluation metrics for machine translation (for example, COMET or BERTScore)
are based on black-box large language models. They often achieve strong
correlations with human judgments, but recent research indicates that the
lower-quality classical metrics remain dominant, one of the potential reasons
being that their decision processes are more transparent. To foster more
widespread acceptance of novel high-quality metrics, explainability thus
becomes crucial. In this concept paper, we identify key properties as well as
key goals of explainable machine translation metrics and provide a
comprehensive synthesis of recent techniques, relating them to our established
goals and properties. In this context, we also discuss the latest
state-of-the-art approaches to explainable metrics based on generative models
such as ChatGPT and GPT4. Finally, we contribute a vision of next-generation
approaches, including natural language explanations. We hope that our work can
help catalyze and guide future research on explainable evaluation metrics and,
mediately, also contribute to better and more transparent machine translation
systems.Comment: Preprint. We published an earlier version of this paper
(arXiv:2203.11131) under a different title. Both versions consider the
conceptualization of explainable metrics and are overall similar. However,
the new version puts a stronger emphasis on the survey of approaches for the
explanation of MT metrics including the latest LLM based approache
Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or semantic confusion. We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated using DBpedia and an enterprise medical KB
Argumentative Explanations for Pattern-Based Text Classifiers
Recent works in Explainable AI mostly address the transparency issue of
black-box models or create explanations for any kind of models (i.e., they are
model-agnostic), while leaving explanations of interpretable models largely
underexplored. In this paper, we fill this gap by focusing on explanations for
a specific interpretable model, namely pattern-based logistic regression (PLR)
for binary text classification. We do so because, albeit interpretable, PLR is
challenging when it comes to explanations. In particular, we found that a
standard way to extract explanations from this model does not consider
relations among the features, making the explanations hardly plausible to
humans. Hence, we propose AXPLR, a novel explanation method using (forms of)
computational argumentation to generate explanations (for outputs computed by
PLR) which unearth model agreements and disagreements among the features.
Specifically, we use computational argumentation as follows: we see features
(patterns) in PLR as arguments in a form of quantified bipolar argumentation
frameworks (QBAFs) and extract attacks and supports between arguments based on
specificity of the arguments; we understand logistic regression as a gradual
semantics for these QBAFs, used to determine the arguments' dialectic strength;
and we study standard properties of gradual semantics for QBAFs in the context
of our argumentative re-interpretation of PLR, sanctioning its suitability for
explanatory purposes. We then show how to extract intuitive explanations (for
outputs computed by PLR) from the constructed QBAFs. Finally, we conduct an
empirical evaluation and two experiments in the context of human-AI
collaboration to demonstrate the advantages of our resulting AXPLR method
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine
learning project, including those involving textual data. We provide a novel
language tool, in the form of a publicly available Python library for
extracting patterns from textual data. The library integrates a first public
implementation of the existing GrASP algorithm. It allows users to extract
patterns using a number of general-purpose built-in linguistic attributes (such
as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged
for the original algorithm, as well as domain-specific custom attributes which
can be incorporated into the library by implementing two functions. The library
is equipped with a web-based interface empowering human users to conveniently
explore data via the extracted patterns, using complementary pattern-centric
and example-centric views: the former includes a reading in natural language
and statistics of each extracted pattern; the latter shows applications of each
extracted pattern to training examples. We demonstrate the usefulness of the
library in classification (spam detection and argument mining), model analysis
(machine translation), and artifact discovery in datasets (SNLI and
20Newsgroups).Comment: Proceedings of Language Resources and Evaluation (LREC), Marseille,
France pp 6093-6103 (2022