80 research outputs found
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing
Cross-lingual semantic parsing transfers parsing capability from a
high-resource language (e.g., English) to low-resource languages with scarce
training data. Previous work has primarily considered silver-standard data
augmentation or zero-shot methods, however, exploiting few-shot gold data is
comparatively unexplored. We propose a new approach to cross-lingual semantic
parsing by explicitly minimizing cross-lingual divergence between probabilistic
latent variables using Optimal Transport. We demonstrate how this direct
guidance improves parsing from natural languages using fewer examples and less
training. We evaluate our method on two datasets, MTOP and MultiATIS++SQL,
establishing state-of-the-art results under a few-shot cross-lingual regime.
Ablation studies further reveal that our method improves performance even
without parallel input translations. In addition, we show that our model better
captures cross-lingual structure in the latent space to improve semantic
representation similarity.Comment: Accepted to TACL 2023. Pre-MIT Press publication. 17 pages, 3
figures, 6 table
Cross-lingual Semantic Parsing with Categorial Grammars
Humans communicate using natural language. We need to make sure that computers can understand us so that they can act on our spoken commands or independently gain new insights from knowledge that is written down as text. A âsemantic parserâ is a program that translates natural-language sentences into computer commands or logical formulasâsomething a computer can work with. Despite much recent progress on semantic parsing, most research focuses on English, and semantic parsers for other languages cannot keep up with the developments. My thesis aims to help close this gap. It investigates âcross-lingual learningâ methods by which a computer can automatically adapt a semantic parser to another language, such as Dutch. The computer learns by looking at example sentences and their translations, e.g., âShe likes to read booksâ/âZe leest graag boekenâ. Even with many such examples, learning which word means what and how word meanings combine into sentence meanings is a challenge, because translations are rarely word-for-word. They exhibit grammatical differences and non-literalities. My thesis presents a method for tackling these challenges based on the grammar formalism Combinatory Categorial Grammar. It shows that this is a suitable formalism for this purpose, that many structural differences between sentences and their translations can be dealt with in this framework, and that a (rudimentary) semantic parser for Dutch can be learned cross-lingually based on one for English. I also investigate methods for building large corpora of texts annotated with logical formulas to further study and improve semantic parsers
Towards Universal Semantic Tagging
The paper proposes the task of universal semantic tagging---tagging word
tokens with language-neutral, semantically informative tags. We argue that the
task, with its independent nature, contributes to better semantic analysis for
wide-coverage multilingual text. We present the initial version of the semantic
tagset and show that (a) the tags provide semantically fine-grained
information, and (b) they are suitable for cross-lingual semantic parsing. An
application of the semantic tagging in the Parallel Meaning Bank supports both
of these points as the tags contribute to formal lexical semantics and their
cross-lingual projection. As a part of the application, we annotate a small
corpus with the semantic tags and present new baseline result for universal
semantic tagging.Comment: 9 pages, International Conference on Computational Semantics (IWCS
GCN-Sem at SemEval-2019 Task 1: Semantic Parsing using Graph Convolutional and Recurrent Neural Networks
This paper describes the system submitted to the SemEval 2019 shared task 1 âCross-lingual Semantic Parsing with UCCAâ. We rely on the semantic dependency parse trees provided in the shared task which are converted from the original UCCA files and model the task as tagging. The aim is to predict the graph structure of the output along with the types of relations among the nodes. Our proposed neural architecture is composed of Graph Convolution and BiLSTM components. The layers of the system share their weights while predicting dependency links and semantic labels. The system is applied to the CONLLU format of the input data and is best suited for semantic dependency parsing
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