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Found 23 papers, 2 papers with code
Efficient Multi-Modal Embeddings from Structured Data
Another motivation for this paper is the growing need for more interpretable models and for evaluating model efficiency regarding size and performance.
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored.
Morphologically Aware Word-Level Translation
We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way.
Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity
Image captioning as a multimodal task has drawn much interest in recent years.
Don't Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction
Human translators routinely have to translate rare inflections of words - due to the Zipfian distribution of words in a language.
What is needed for simple spatial language capabilities in VQA?
Visual question answering (VQA) comprises a variety of language capabilities.
The Meaning of ``Most'' for Visual Question Answering Models
The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms.
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs
Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts.
The meaning of "most" for visual question answering models
The correct interpretation of quantifier statements in the context of a visual scene requires non-trivial inference mechanisms.
How clever is the FiLM model, and how clever can it be?
The FiLM model achieves close-to-perfect performance on the diagnostic CLEVR dataset and is distinguished from other such models by having a comparatively simple and easily transferable architecture.
Semantic Composition via Probabilistic Model Theory
Semantic composition remains an open problem for vector space models of semantics.
Variational Inference for Logical Inference
Functional Distributional Semantics is a framework that aims to learn, from text, semantic representations which can be interpreted in terms of truth.
Deep learning evaluation using deep linguistic processing
We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice.
ShapeWorld - A new test methodology for multimodal language understanding
We introduce a novel framework for evaluating multimodal deep learning models with respect to their language understanding and generalization abilities.
Functional Distributional Semantics
Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena.
Resources for building applications with Dependency Minimal Recursion Semantics
We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium.
TagNText: A parallel corpus for the induction of resource-specific non-taxonomical relations from tagged images
In particular, our work attempts to answer the following questions: if users were to use full descriptions, would their current tags be words present in these hypothetical sentences?
Rhetorical Move Detection in English Abstracts: Multi-label Sentence Classifiers and their Annotated Corpora
Here, we present MAZEA (Multi-label Argumentative Zoning for English Abstracts), a multi-label classifier which automatically identifies rhetorical moves in abstracts but allows for a given sentence to be assigned as many labels as appropriate.
