27 research outputs found
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction with
three contributions: (i) We show that deep, Abstract Meaning Representations
(AMR) significantly improve the accuracy of a biomolecular interaction
extraction system when compared to a baseline that relies solely on surface-
and syntax-based features; (ii) In contrast with previous approaches that infer
relations on a sentence-by-sentence basis, we expand our framework to enable
consistent predictions over sets of sentences (documents); (iii) We further
modify and expand a graph kernel learning framework to enable concurrent
exploitation of automatically induced AMR (semantic) and dependency structure
(syntactic) representations. Our experiments show that our approach yields
interaction extraction systems that are more robust in environments where there
is a significant mismatch between training and test conditions.Comment: Appearing in Proceedings of the Thirtieth AAAI Conference on
Artificial Intelligence (AAAI-16
Polyglot Semantic Parsing in APIs
Traditional approaches to semantic parsing (SP) work by training individual
models for each available parallel dataset of text-meaning pairs. In this
paper, we explore the idea of polyglot semantic translation, or learning
semantic parsing models that are trained on multiple datasets and natural
languages. In particular, we focus on translating text to code signature
representations using the software component datasets of Richardson and Kuhn
(2017a,b). The advantage of such models is that they can be used for parsing a
wide variety of input natural languages and output programming languages, or
mixed input languages, using a single unified model. To facilitate modeling of
this type, we develop a novel graph-based decoding framework that achieves
state-of-the-art performance on the above datasets, and apply this method to
two other benchmark SP tasks.Comment: accepted for NAACL-2018 (camera ready version
Towards End-User Development for IoT: A Case Study on Semantic Parsing of Cooking Recipes for Programming Kitchen Devices
Semantic parsing of user-generated instructional text, in the way of enabling
end-users to program the Internet of Things (IoT), is an underexplored area. In
this study, we provide a unique annotated corpus which aims to support the
transformation of cooking recipe instructions to machine-understandable
commands for IoT devices in the kitchen. Each of these commands is a tuple
capturing the semantics of an instruction involving a kitchen device in terms
of "What", "Where", "Why" and "How". Based on this corpus, we developed machine
learning-based sequence labelling methods, namely conditional random fields
(CRF) and a neural network model, in order to parse recipe instructions and
extract our tuples of interest from them. Our results show that while it is
feasible to train semantic parsers based on our annotations, most
natural-language instructions are incomplete, and thus transforming them into
formal meaning representation, is not straightforward.Comment: 8 pages, 1 figure, 2 tables. Work completed in January 202
Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing
Prior work in semantic parsing has shown that conventional seq2seq models
fail at compositional generalization tasks. This limitation led to a resurgence
of methods that model alignments between sentences and their corresponding
meaning representations, either implicitly through latent variables or
explicitly by taking advantage of alignment annotations. We take the second
direction and propose TPol, a two-step approach that first translates input
sentences monotonically and then reorders them to obtain the correct output.
This is achieved with a modular framework comprising a Translator and a
Reorderer component. We test our approach on two popular semantic parsing
datasets. Our experiments show that by means of the monotonic translations,
TPol can learn reliable lexico-logical patterns from aligned data,
significantly improving compositional generalization both over conventional
seq2seq models, as well as over a recently proposed approach that exploits gold
alignments.Comment: 8 pages, 4 figures, 4 table