909 research outputs found
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
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet
its success relies on the availability of large amounts of supervised data. Our
goal is to learn a neural semantic parser when only prior knowledge about a
limited number of simple rules is available, without access to either annotated
programs or execution results. Our approach is initialized by rules, and
improved in a back-translation paradigm using generated question-program pairs
from the semantic parser and the question generator. A phrase table with
frequent mapping patterns is automatically derived, also updated as training
progresses, to measure the quality of generated instances. We train the model
with model-agnostic meta-learning to guarantee the accuracy and stability on
examples covered by rules, and meanwhile acquire the versatility to generalize
well on examples uncovered by rules. Results on three benchmark datasets with
different domains and programs show that our approach incrementally improves
the accuracy. On WikiSQL, our best model is comparable to the SOTA system
learned from denotations
The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory
We describe the first steps in the development of an artificial agent focused
on the Brazilian maritime territory, a large region within the South Atlantic
also known as the Blue Amazon. The "BLue Amazon Brain" (BLAB) integrates a
number of services aimed at disseminating information about this region and its
importance, functioning as a tool for environmental awareness. The main service
provided by BLAB is a conversational facility that deals with complex questions
about the Blue Amazon, called BLAB-Chat; its central component is a controller
that manages several task-oriented natural language processing modules (e.g.,
question answering and summarizer systems). These modules have access to an
internal data lake as well as to third-party databases. A news reporter
(BLAB-Reporter) and a purposely-developed wiki (BLAB-Wiki) are also part of the
BLAB service architecture. In this paper, we describe our current version of
BLAB's architecture (interface, backend, web services, NLP modules, and
resources) and comment on the challenges we have faced so far, such as the lack
of training data and the scattered state of domain information. Solving these
issues presents a considerable challenge in the development of artificial
intelligence for technical domains
A Sign Language to Text Converter Using Leap Motion
This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques
Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot
Text-to-GQL (Text2GQL) is a task that converts the user's questions into GQL (Graph Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human–Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GQL task. This solution uses the Adapter pre-trained by “the linking of GQL schemas and the corresponding utterances" as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human–machine language translation task. In the study, the proposed Text2GQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios
First CLIPS Conference Proceedings, volume 2
The topics of volume 2 of First CLIPS Conference are associated with following applications: quality control; intelligent data bases and networks; Space Station Freedom; Space Shuttle and satellite; user interface; artificial neural systems and fuzzy logic; parallel and distributed processing; enchancements to CLIPS; aerospace; simulation and defense; advisory systems and tutors; and intelligent control
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