105 research outputs found
Stochastic phonological grammars and acceptability
In foundational works of generative phonology it is claimed that subjects can
reliably discriminate between possible but non-occurring words and words that
could not be English. In this paper we examine the use of a probabilistic
phonological parser for words to model experimentally-obtained judgements of
the acceptability of a set of nonsense words. We compared various methods of
scoring the goodness of the parse as a predictor of acceptability. We found
that the probability of the worst part is not the best score of acceptability,
indicating that classical generative phonology and Optimality Theory miss an
important fact, as these approaches do not recognise a mechanism by which the
frequency of well-formed parts may ameliorate the unacceptability of
low-frequency parts. We argue that probabilistic generative grammars are
demonstrably a more psychologically realistic model of phonological competence
than standard generative phonology or Optimality Theory.Comment: compressed postscript, 8 pages, 1 figur
Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models
Vector space models of word meaning all share the assumption that words
occurring in similar contexts have similar meanings. In such models, words that
are similar in their topical associations but differ in their logical force
tend to emerge as semantically close, creating well-known challenges for NLP
applications that involve logical reasoning. Modern pretrained language models,
such as BERT, RoBERTa and GPT-3 hold the promise of performing better on
logical tasks than classic static word embeddings. However, reports are mixed
about their success. In the current paper, we advance this discussion through a
systematic study of scalar adverbs, an under-explored class of words with
strong logical force. Using three different tasks, involving both naturalistic
social media data and constructed examples, we investigate the extent to which
BERT, RoBERTa, GPT-2 and GPT-3 exhibit general, human-like, knowledge of these
common words. We ask: 1) Do the models distinguish amongst the three semantic
categories of MODALITY, FREQUENCY and DEGREE? 2) Do they have implicit
representations of full scales from maximally negative to maximally positive?
3) How do word frequency and contextual factors impact model performance? We
find that despite capturing some aspects of logical meaning, the models fall
far short of human performance.Comment: Published in BlackBoxNLP workshop, EMNLP 202
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The Meaning of Intonational Contours in the Interpretation of Discourse
Recent investigations of the contribution that intonation makes to overall utterance and discourse interpretation promise new sources of information for the investigation of long-time concerns in NLP. In Hirschberg & Pierrehumber 1986 we proposed that intonational features such as phrasing, accent placement, pitch range, and tune represent important sources of information about the attentional and intentional structures of discourse. In this paper we examine the particular contribution of choice of tune, or intonational contour, to discourse interpretation
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model
Can pretrained language models (PLMs) generate derivationally complex words?
We present the first study investigating this question, taking BERT as the
example PLM. We examine BERT's derivational capabilities in different settings,
ranging from using the unmodified pretrained model to full finetuning. Our best
model, DagoBERT (Derivationally and generatively optimized BERT), clearly
outperforms the previous state of the art in derivation generation (DG).
Furthermore, our experiments show that the input segmentation crucially impacts
BERT's derivational knowledge, suggesting that the performance of PLMs could be
further improved if a morphologically informed vocabulary of units were used
Predicting the Growth of Morphological Families from Social and Linguistic Factors
We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as “trump”, “antitrumpism”, and “detrumpify”, in social media. We introduce the novel task of Morphological Family Expansion Predic- tion (MFEP) as predicting the increase in the size of a morphological family. We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark. Our experiments demonstrate very good performance on MFEP
The Reddit Politosphere: A Large-Scale Text and NetworkResource of Online Political Discourse
We introduce the Reddit Politosphere, a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. It is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data, allowing for methodologically-diverse analyses. We describe in detail how we create the Reddit Politosphere, present descriptive statistics, and sketch potential directions for future research based on the resource
A Graph Auto-encoder Model of Derivational Morphology
There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics (Bauer, 2019). We present a graph auto-encoder that learns em- beddings capturing information about the com- patibility of affixes and stems in derivation. The auto-encoder models MWF in English sur- prisingly well by combining syntactic and se- mantic information with associative informa- tion from the mental lexicon
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