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Found 23 papers, 9 papers with code
Predicting scalar diversity with context-driven uncertainty over alternatives
Here, we test the hypothesis that SI rates depend on the listener’s confidence in the underlying scale, which we operationalize as uncertainty over the distribution of possible alternatives conditioned on the context.
Code Pretraining Improves Entity Tracking Abilities of Language Models
Recent work has provided indirect evidence that pretraining language models on code improves the ability of models to track state changes of discourse entities expressed in natural language.
Scope Ambiguities in Large Language Models
Sentences containing multiple semantic operators with overlapping scope often create ambiguities in interpretation, known as scope ambiguities.
Entity Tracking in Language Models
We use this task to first investigate whether Flan-T5, GPT-3 and GPT-3. 5 can track the state of entities, and find that only GPT-3. 5 models, which have been pretrained on large amounts of code, exhibit this ability.
Expectations over Unspoken Alternatives Predict Pragmatic Inferences
Here, we test a shared mechanism explaining SI rates within and across scales: context-driven expectations about the unspoken alternatives.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it
We use this evaluation suite for a fine-grained investigation of the entity tracking abilities of the Transformer-based models GPT-2 and GPT-3.
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models
We find that pre-trained seq2seq models generalize hierarchically when performing syntactic transformations, whereas models trained from scratch on syntactic transformations do not.
NOPE: A Corpus of Naturally-Occurring Presuppositions in English
Understanding language requires grasping not only the overtly stated content, but also making inferences about things that were left unsaid.
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework.
Harnessing the linguistic signal to predict scalar inferences
Pragmatic inferences often subtly depend on the presence or absence of linguistic features.
Enhancing Universal Dependency Treebanks: A Case Study
We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies.
Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog
We use this data set to evaluate three different cross-lingual transfer methods: (1) translating the training data, (2) using cross-lingual pre-trained embeddings, and (3) a novel method of using a multilingual machine translation encoder as contextual word representations.
Crowdsourcing a Large Corpus of Clickbait on Twitter
To address the urging task of clickbait detection, we constructed a new corpus of 38, 517 annotated Twitter tweets, the Webis Clickbait Corpus 2017.
Sentences with Gapping: Parsing and Reconstructing Elided Predicates
Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks
Many shallow natural language understanding tasks use dependency trees to extract relations between content words.
