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Found 35 papers, 5 papers with code
On Learning Interpreted Languages with Recurrent Models
Can recurrent neural nets, inspired by human sequential data processing, learn to understand language?
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings
Word embeddings have advanced the state of the art in NLP across numerous tasks.
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches
This paper presents our work in long and short form choice, a significant question of lexical choice, which plays an important role in many Natural Language Understanding tasks.
Grounded and Well-rounded: A Methodological Approach to the Study of Cross-modal and Cross-lingual Grounding
Grounding has been argued to be a crucial component towards the development of more complete and truly semantically competent artificial intelligence systems.
The Scenario Refiner: Grounding subjects in images at the morphological level
Derivationally related words, such as "runner" and "running", exhibit semantic differences which also elicit different visual scenarios.
Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations
Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models.
Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering
In many real-world scenarios, the absence of external knowledge source like Wikipedia restricts question answering systems to rely on latent internal knowledge in limited dialogue data.
Generating image captions with external encyclopedic knowledge
Our approach includes a novel way of using image location to identify relevant open-domain facts in an external knowledge base, with their subsequent integration into the captioning pipeline at both the encoding and decoding stages.
How to Dissect a Muppet: The Structure of Transformer Embedding Spaces
Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm.
Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings
Word embeddings have advanced the state of the art in NLP across numerous tasks.
A Game Interface to Study Semantic Grounding in Text-Based Models
Can language models learn grounded representations from text distribution alone?
What Meaning-Form Correlation Has to Compose With
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive.
Geo-Aware Image Caption Generation
Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge.
What Meaning-Form Correlation Has to Compose With: A Study of MFC on Artificial and Natural Language
Compositionality is a widely discussed property of natural languages, although its exact definition has been elusive.
G\'en\'eration automatique de d\'efinitions pour le fran\ccais (Definition Modeling in French)
La g{\'e}n{\'e}ration de d{\'e}finitions est une t{\^a}che r{\'e}cente qui vise {\`a} produire des d{\'e}finitions lexicographiques {\`a} partir de plongements lexicaux.
Mark my Word: A Sequence-to-Sequence Approach to Definition Modeling
Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations.
Choosing between Long and Short Word Forms in Mandarin
Between 80{\%} and 90{\%} of all Chinese words have long and short form such as 老虎/虎 (lao-hu/hu , tiger) (Duanmu:2013).
On learning an interpreted language with recurrent models
Can recurrent neural nets, inspired by human sequential data processing, learn to understand language?
SemEval-2018 Task 10: Capturing Discriminative Attributes
This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes.
RUSSE: The First Workshop on Russian Semantic Similarity
The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference.
Human and Machine Judgements for Russian Semantic Relatedness
On the one hand, humans easily make judgments about semantic relatedness.
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.
Typology of Adjectives Benchmark for Compositional Distributional Models
In this paper we present a novel application of compositional distributional semantic models (CDSMs): prediction of lexical typology.
Deriving Boolean structures from distributional vectors
Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up.
