We went to look for meaning and all we got were these lousy representations: aspects of meaning representation for computational semantics
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Abstract (arXiv)
In this paper we examine different meaning representations that are commonly used in different natural language applications today and discuss their limits, both in terms of the aspects of the natural language meaning they are modelling and in terms of the aspects of the application for which they are used.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 134 references that are in the map. First posted on arXiv 10 Sep 2021.
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39 of 134 references.
References · 39
- 2017Attention Is All You Needcited by 59,932
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
- 2015VQA: Visual Question Answeringcited by 2,489
- 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
- 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
- 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
- 2017Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environmentscited by 1,013
- 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 998
- 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
- 2014Show and Tell: A Neural Image Caption Generatorcited by 877
- 2019How multilingual is Multilingual BERT?cited by 642
27 more
- 2017Word Translation Without Parallel Datacited by 480
- 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 452
- 2016Visual Dialogcited by 377
- 2017Embodied Question Answeringcited by 335
- 2018SentEval: An Evaluation Toolkit for Universal Sentence Representationscited by 294
- 2016Hierarchical Question-Image Co-Attention for Visual Question Answeringcited by 278
- 2020Experience Grounds Languagecited by 241
- 2016Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioningcited by 231
- 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
- 2019RUBi: Reducing Unimodal Biases in Visual Question Answeringcited by 183
- 2019Image Captioning: Transforming Objects into Wordscited by 171
- 2018A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddingscited by 145
- 2016GuessWhat?! Visual object discovery through multi-modal dialoguecited by 112
- 2018Diachronic word embeddings and semantic shifts: a surveycited by 90
- 2018Visual Coreference Resolution in Visual Dialog using Neural Module Networkscited by 54
- 2016An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generationcited by 48
- 2019Human few-shot learning of compositional instructionscited by 47
- 2019The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialoguecited by 46
- 2020Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similaritycited by 38
- 2016Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measurescited by 36
- 2019Correlating neural and symbolic representations of languagecited by 32
- 2018Short-Term Meaning Shift: A Distributional Explorationcited by 23
- 2016Continuous multilinguality with language vectorscited by 20
- 2019MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environmentcited by 19
- 2018A State-of-the-Art of Semantic Change Computationcited by 14
- 2019Grounded Agreement Games: Emphasizing Conversational Grounding in Visual Dialogue Settingscited by 10
- 2020How to marry a star: probabilistic constraints for meaning in contextcited by 5
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