Does referent predictability affect the choice of referential form? A
computational approach using masked coreference resolution
- URL: http://arxiv.org/abs/2109.13105v1
- Date: Mon, 27 Sep 2021 14:54:46 GMT
- Title: Does referent predictability affect the choice of referential form? A
computational approach using masked coreference resolution
- Authors: Laura Aina, Xixian Liao, Gemma Boleda and Matthijs Westera
- Abstract summary: We study the dynamics of referring expressions using novel computational estimates of referent predictability.
A statistical analysis of the relationship between model output and mention form supports the hypothesis that predictability affects the form of a mention.
- Score: 10.73926355134268
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: It is often posited that more predictable parts of a speaker's meaning tend
to be made less explicit, for instance using shorter, less informative words.
Studying these dynamics in the domain of referring expressions has proven
difficult, with existing studies, both psycholinguistic and corpus-based,
providing contradictory results. We test the hypothesis that speakers produce
less informative referring expressions (e.g., pronouns vs. full noun phrases)
when the context is more informative about the referent, using novel
computational estimates of referent predictability. We obtain these estimates
training an existing coreference resolution system for English on a new task,
masked coreference resolution, giving us a probability distribution over
referents that is conditioned on the context but not the referring expression.
The resulting system retains standard coreference resolution performance while
yielding a better estimate of human-derived referent predictability than
previous attempts. A statistical analysis of the relationship between model
output and mention form supports the hypothesis that predictability affects the
form of a mention, both its morphosyntactic type and its length.
Related papers
- Rethinking Distance Metrics for Counterfactual Explainability [53.436414009687]
We investigate a framing for counterfactual generation methods that considers counterfactuals not as independent draws from a region around the reference, but as jointly sampled with the reference from the underlying data distribution.
We derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings.
arXiv Detail & Related papers (2024-10-18T15:06:50Z) - On the Role of Context in Reading Time Prediction [50.87306355705826]
We present a new perspective on how readers integrate context during real-time language comprehension.
Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit is an affine function of its in-context information content.
arXiv Detail & Related papers (2024-09-12T15:52:22Z) - Statistical Uncertainty in Word Embeddings: GloVe-V [35.04183792123882]
We introduce a method to obtain approximate, easy-to-use, and scalable reconstruction error variance estimates for GloVe.
To demonstrate the value of embeddings with variance (GloVe-V), we illustrate how our approach enables principled hypothesis testing in core word embedding tasks.
arXiv Detail & Related papers (2024-06-18T00:35:02Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - Logical Satisfiability of Counterfactuals for Faithful Explanations in
NLI [60.142926537264714]
We introduce the methodology of Faithfulness-through-Counterfactuals.
It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation.
It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
arXiv Detail & Related papers (2022-05-25T03:40:59Z) - SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption
Evaluation via Typicality Analysis [20.026835809227283]
We introduce "typicality", a new formulation of evaluation rooted in information theory.
We show how these decomposed dimensions of semantics and fluency provide greater system-level insight into captioner differences.
Our proposed metrics along with their combination, SMURF, achieve state-of-the-art correlation with human judgment when compared with other rule-based evaluation metrics.
arXiv Detail & Related papers (2021-06-02T19:58:20Z) - Exploring Lexical Irregularities in Hypothesis-Only Models of Natural
Language Inference [5.283529004179579]
Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences.
Models that understand entailment should encode both, the premise and the hypothesis.
Experiments by Poliak et al. revealed a strong preference of these models towards patterns observed only in the hypothesis.
arXiv Detail & Related papers (2021-01-19T01:08:06Z) - Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
We propose a novel scalable method to learn double-robust representations for counterfactual predictions.
We make robust and efficient counterfactual predictions for both individual and average treatment effects.
The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
arXiv Detail & Related papers (2020-10-15T16:39:26Z) - Achieving Equalized Odds by Resampling Sensitive Attributes [13.114114427206678]
We present a flexible framework for learning predictive models that approximately satisfy the equalized odds notion of fairness.
This differentiable functional is used as a penalty driving the model parameters towards equalized odds.
We develop a formal hypothesis test to detect whether a prediction rule violates this property, the first such test in the literature.
arXiv Detail & Related papers (2020-06-08T00:18:34Z) - Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
We establish a novel set of evaluation criteria for such feature based explanations by analysis.
We obtain new explanations that are loosely necessary and sufficient for a prediction.
We extend the explanation to extract the set of features that would move the current prediction to a target class.
arXiv Detail & Related papers (2020-05-31T05:52:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.