17 research outputs found
Prompting for a conversation: How to control a dialog model?
Dialog modelling faces a difficult trade-off. Models are trained on a large
amount of text, yet their responses need to be limited to a desired scope and
style of a dialog agent. Because the datasets used to achieve the former
contain language that is not compatible with the latter, pre-trained dialog
models are fine-tuned on smaller curated datasets. However, the fine-tuning
process robs them of the ability to produce diverse responses, eventually
reducing them to dull conversation partners. In this paper we investigate if
prompting can mitigate the above trade-off. Specifically, we experiment with
conditioning the prompt on the query, rather than training a single prompt for
all queries. By following the intuition that freezing the pre-trained language
model will conserve its expressivity, we find that compared to fine-tuning,
prompting can achieve a higher BLEU score and substantially improve the
diversity and novelty of the responses
Benchmarking Compositionality with Formal Languages
Recombining known primitive concepts into larger novel combinations is a
quintessentially human cognitive capability. Whether large neural models in NLP
can acquire this ability while learning from data is an open question. In this
paper, we investigate this problem from the perspective of formal languages. We
use deterministic finite-state transducers to make an unbounded number of
datasets with controllable properties governing compositionality. By randomly
sampling over many transducers, we explore which of their properties contribute
to learnability of a compositional relation by a neural network. We find that
the models either learn the relations completely or not at all. The key is
transition coverage, setting a soft learnability limit at 400 examples per
transition
An Ordinal Latent Variable Model of Conflict Intensity
For the quantitative monitoring of international relations, political events
are extracted from the news and parsed into "who-did-what-to-whom" patterns.
This has resulted in large data collections which require aggregate statistics
for analysis. The Goldstein Scale is an expert-based measure that ranks
individual events on a one-dimensional scale from conflictual to cooperative.
However, the scale disregards fatality counts as well as perpetrator and victim
types involved in an event. This information is typically considered in
qualitative conflict assessment. To address this limitation, we propose a
probabilistic generative model over the full
subject-predicate-quantifier-object tuples associated with an event. We treat
conflict intensity as an interpretable, ordinal latent variable that correlates
conflictual event types with high fatality counts. Taking a Bayesian approach,
we learn a conflict intensity scale from data and find the optimal number of
intensity classes. We evaluate the model by imputing missing data. Our scale
proves to be more informative than the original Goldstein Scale in
autoregressive forecasting and when compared with global online attention
towards armed conflicts
UniMorph 4.0:Universal Morphology
The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet