12 research outputs found
Non-Compositionality in Sentiment: New Data and Analyses
When natural language phrases are combined, their meaning is often more than
the sum of their parts. In the context of NLP tasks such as sentiment analysis,
where the meaning of a phrase is its sentiment, that still applies. Many NLP
studies on sentiment analysis, however, focus on the fact that sentiment
computations are largely compositional. We, instead, set out to obtain
non-compositionality ratings for phrases with respect to their sentiment. Our
contributions are as follows: a) a methodology for obtaining those
non-compositionality ratings, b) a resource of ratings for 259 phrases --
NonCompSST -- along with an analysis of that resource, and c) an evaluation of
computational models for sentiment analysis using this new resource.Comment: Published in EMNLP Findings 2023; 13 pages total (5 in the main
paper, 3 pages with limitations, acknowledgments and references, 5 pages with
appendices
Transcoding compositionally: using attention to find more generalizable solutions
While sequence-to-sequence models have shown remarkable generalization power
across several natural language tasks, their construct of solutions are argued
to be less compositional than human-like generalization. In this paper, we
present seq2attn, a new architecture that is specifically designed to exploit
attention to find compositional patterns in the input. In seq2attn, the two
standard components of an encoder-decoder model are connected via a transcoder,
that modulates the information flow between them. We show that seq2attn can
successfully generalize, without requiring any additional supervision, on two
tasks which are specifically constructed to challenge the compositional skills
of neural networks. The solutions found by the model are highly interpretable,
allowing easy analysis of both the types of solutions that are found and
potential causes for mistakes. We exploit this opportunity to introduce a new
paradigm to test compositionality that studies the extent to which a model
overgeneralizes when confronted with exceptions. We show that seq2attn exhibits
such overgeneralization to a larger degree than a standard sequence-to-sequence
model.Comment: to appear at BlackboxNLP 2019, AC
Text Characterization Toolkit
In NLP, models are usually evaluated by reporting single-number performance
scores on a number of readily available benchmarks, without much deeper
analysis. Here, we argue that - especially given the well-known fact that
benchmarks often contain biases, artefacts, and spurious correlations - deeper
results analysis should become the de-facto standard when presenting new models
or benchmarks. We present a tool that researchers can use to study properties
of the dataset and the influence of those properties on their models'
behaviour. Our Text Characterization Toolkit includes both an easy-to-use
annotation tool, as well as off-the-shelf scripts that can be used for specific
analyses. We also present use-cases from three different domains: we use the
tool to predict what are difficult examples for given well-known trained models
and identify (potentially harmful) biases and heuristics that are present in a
dataset
Meta-learning for fast cross-lingual adaptation in dependency parsing
Meta-learning, or learning to learn, is a technique that can help to overcome
resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to
new tasks. We apply model-agnostic meta-learning (MAML) to the task of
cross-lingual dependency parsing. We train our model on a diverse set of
languages to learn a parameter initialization that can adapt quickly to new
languages. We find that meta-learning with pre-training can significantly
improve upon the performance of language transfer and standard supervised
learning baselines for a variety of unseen, typologically diverse, and
low-resource languages, in a few-shot learning setup
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
Recommended from our members
Episodic memory demands modulate novel metaphor use during event narration
Metaphor is an important part of everyday thought and language. Although we are often not aware of metaphor in everyday speech, on occasion, a particularly creative or novel use of metaphor will make us pay attention. It has been hypothesized that one of the driving cognitive factors behind the use of novel metaphor is a need to describe a new reality (as opposed to a preexisting reality) that would otherwise be difficult to convey using conventionalized metaphor. To this extent, novel metaphor use in everyday language may be more associated with episodic memory demands in contrast to conventional metaphor that is associated with semantic memory. To test this idea we analyzed novel metaphor use in the Hippocorpus --- a corpus of more than 5000 recalled and imagined stories about memorable life events in the first person perspective. In this dataset, recalled events have been shown to rely on episodic memory to a greater extent than descriptions of imagined events (i.e., narrating an event as if it happened to you but not describing an event that actually happened to you), which largely draw on semantic memory. We hypothesized that novel metaphor use during event narration should be modulated by the extent to which language users are able to draw on primary experience to describe events. We found that novel metaphor counts in recalled events were significantly higher than imagined events. Importantly, we found that factors that influence the extent to which language users are able to draw on primary experience during event narration (i.e., openness to experience, similarity to one's own experience, and how memorable or important an event was) modulated novel metaphor use in different ways in imagined compared to recalled events. The work paves the way for using large scale corpora to analyze underlying cognitive processes that modulate metaphorical language use
A taxonomy and review of generalization research in NLP
Funding Information: We thank A. Williams, A. Joulin, E. Bruni, L. Weber, R. Kirk and S. Riedel for providing feedback on the various stages of this paper, and G. Marcus for providing detailed feedback on the final draft. We also thank the reviewers of our work for providing useful comments. We thank E. Hupkes for making the app that allows searching through references, and we thank D. Haziza and E. Takmaz for other contributions to the website. M.G. was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 819455). V.D. was supported by the UKRI Centre for Doctoral Training in Natural Language Processing, funded by the UKRI (grant no. EP/S022481/1) and the University of Edinburgh. N.S. was supported by the Hyundai Motor Company (under the project Uncertainty in Neural Sequence Modeling) and the Samsung Advanced Institute of Technology (under the project Next Generation Deep Learning: From Pattern Recognition to AI). Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD