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Found 43 papers, 10 papers with code
Controlled tasks for model analysis: Retrieving discrete information from sequences
In recent years, the NLP community has shown increasing interest in analysing how deep learning models work.
Challenges in including extra-linguistic context in pre-trained language models
To successfully account for language, computational models need to take into account both the linguistic context (the content of the utterances) and the extra-linguistic context (for instance, the participants in a dialogue).
The Impact of Familiarity on Naming Variation: A Study on Object Naming in Mandarin Chinese
Different speakers often produce different names for the same object or entity (e. g., "woman" vs. "tourist" for a female tourist).
Run Like a Girl! Sports-Related Gender Bias in Language and Vision
A computational model trained on these naming data reproduces the bias.
Communication breakdown: On the low mutual intelligibility between human and neural captioning
We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner.
Does referent predictability affect the choice of referential form? A computational approach using masked coreference resolution
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.
Humans Meet Models on Object Naming: A New Dataset and Analysis
We also find that standard evaluations underestimate the actual effectiveness of the naming model: on the single-label names of the original dataset (Visual Genome), it obtains −27{\%} accuracy points than on MN v2, that includes all valid object names.
Probing for Referential Information in Language Models
Language models keep track of complex information about the preceding context {--} including, e. g., syntactic relations in a sentence.
Object Naming in Language and Vision: A Survey and a New Dataset
We highlight the challenges involved and provide a preliminary analysis of the ManyNames data, showing that there is a high level of agreement in naming, on average.
Deep daxes: Mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
We find that constraints in both learning and selection can foster mutual exclusivity, as long as they put words in competition for lexical meaning.
Recurrent Instance Segmentation using Sequences of Referring Expressions
The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions).
Putting words in context: LSTM language models and lexical ambiguity
In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers.
Don't Blame Distributional Semantics if it can't do Entailment
Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
In this paper we analyze the behavior of two recently proposed entity-centric models in a referential task, Entity Linking in Multi-party Dialogue (SemEval 2018 Task 4).
Distributional Semantics and Linguistic Theory
Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown in a large body of work in computational linguistics; yet, its impact in theoretical linguistics has so far been limited.
How to represent a word and predict it, too: Improving tied architectures for language modelling
Recent state-of-the-art neural language models share the representations of words given by the input and output mappings.
Short-Term Meaning Shift: A Distributional Exploration
We present the first exploration of meaning shift over short periods of time in online communities using distributional representations.
Instantiation
Our paper closes this gap by investigating and modeling the lexical relation of instantiation, which holds between an entity-denoting and a category-denoting expression (Marie Curie -- scientist or Mumbai -- city).
AMORE-UPF at SemEval-2018 Task 4: BiLSTM with Entity Library
This paper describes our winning contribution to SemEval 2018 Task 4: Character Identification on Multiparty Dialogues.
Talking about the world with a distributed model
We use language to talk about the world, and so reference is a crucial property of language.
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible
Word embeddings are supposed to provide easy access to semantic relations such as {``}male of{''} (man{--}woman).
Instances and concepts in distributional space
Instances ({``}Mozart{''}) are ontologically distinct from concepts or classes ({``}composer{''}).
Living a discrete life in a continuous world: Reference with distributed representations
Reference is a crucial property of language that allows us to connect linguistic expressions to the world.
"Show me the cup": Reference with Continuous Representations
One of the most basic functions of language is to refer to objects in a shared scene.
The LAMBADA dataset: Word prediction requiring a broad discourse context
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task.
Zipf's law for word frequencies: word forms versus lemmas in long texts
In all cases Zipf's law is fulfilled, in the sense that a power-law distribution of word or lemma frequencies is valid for several orders of magnitude.
