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Found 15 papers, 4 papers with code
Grammar-Based Grounded Lexicon Learning
We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a compositional and grounded meaning representation of language from grounded data, such as paired images and texts.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing
We present substructure distribution projection (SubDP), a technique that projects a distribution over structures in one domain to another, by projecting substructure distributions separately.
Substructure Substitution: Structured Data Augmentation for NLP
We study a family of data augmentation methods, substructure substitution (SUB2), for natural language processing (NLP) tasks.
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment
Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces.
Deep Clustering of Text Representations for Supervision-free Probing of Syntax
We explore deep clustering of text representations for unsupervised model interpretation and induction of syntax.
On the Role of Supervision in Unsupervised Constituency Parsing
We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing $F_1$ score on the Wall Street Journal (WSJ) development set (1, 700 sentences).
A Cross-Task Analysis of Text Span Representations
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution.
Visually Grounded Neural Syntax Acquisition
We define concreteness of constituents by their matching scores with images, and use it to guide the parsing of text.
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings
In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors.
On Tree-Based Neural Sentence Modeling
To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i. e., binary balanced tree, left-branching tree and right-branching tree) in the encoders.
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Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
Begin with an insightful adversarial attack on VSE embeddings, we show the limitation of current frameworks and image-text datasets (e. g., MS-COCO) both quantitatively and qualitatively.
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis
Unsupervised learned representations of polysemous words generate a large of pseudo multi senses since unsupervised methods are overly sensitive to contextual variations.
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation
Previous researches have shown that learning multiple representations for polysemous words can improve the performance of word embeddings on many tasks.
