6 research outputs found
Automatic Accuracy Prediction for AMR Parsing
Abstract Meaning Representation (AMR) represents sentences as directed,
acyclic and rooted graphs, aiming at capturing their meaning in a machine
readable format. AMR parsing converts natural language sentences into such
graphs. However, evaluating a parser on new data by means of comparison to
manually created AMR graphs is very costly. Also, we would like to be able to
detect parses of questionable quality, or preferring results of alternative
systems by selecting the ones for which we can assess good quality. We propose
AMR accuracy prediction as the task of predicting several metrics of
correctness for an automatically generated AMR parse - in absence of the
corresponding gold parse. We develop a neural end-to-end multi-output
regression model and perform three case studies: firstly, we evaluate the
model's capacity of predicting AMR parse accuracies and test whether it can
reliably assign high scores to gold parses. Secondly, we perform parse
selection based on predicted parse accuracies of candidate parses from
alternative systems, with the aim of improving overall results. Finally, we
predict system ranks for submissions from two AMR shared tasks on the basis of
their predicted parse accuracy averages. All experiments are carried out across
two different domains and show that our method is effective.Comment: accepted at *SEM 201
An empirical evaluation of AMR parsing for legal documents
Many approaches have been proposed to tackle the problem of Abstract Meaning
Representation (AMR) parsing, helps solving various natural language processing
issues recently. In our paper, we provide an overview of different methods in
AMR parsing and their performances when analyzing legal documents. We conduct
experiments of different AMR parsers on our annotated dataset extracted from
the English version of Japanese Civil Code. Our results show the limitations as
well as open a room for improvements of current parsing techniques when
applying in this complicated domain
Modeling Meaning for Description and Interaction
Language is a powerful tool for communication and coordination, allowing us to share thoughts, ideas, and instructions with others. Accordingly, enabling people to communicate linguistically with digital agents has been among the longest-standing goals in artificial intelligence (AI). However, unlike humans, machines do not naturally acquire the ability to extract meaning from language.
One natural solution to this problem is to represent meaning in a structured format and then develop models for processing language into such structures. Unlike natural language, these structured representations can be directly processed and interpreted by existing algorithms. Indeed, much of the digital infrastructure we have built is mediated by structured representations (e.g. programs and APIs). Furthermore, unlike the internal representations of current neural models, structured representations are built to be used and interpreted by people. I focus on methods for parsing language into these dually-interpretable representations of meaning. I introduce models that learn to predict structure from language and apply them to a variety of tasks, ranging from linguistic description to interaction with robots and digital assistants.
I address three thematic challenges in modeling meaning: abstraction, sensitivity, and ambiguity. In order to be useful, meaning representations must abstract away from the linguistic input. Abstractions differ for each representation used, and must be learned by the model. The process of abstraction entails a kind of invariance: different linguistic inputs mapping to the same meaning. At the same time, meaning is sensitive to slight changes in the linguistic input; here, similar inputs might map to very different meanings. Finally, language is often ambiguous, and many utterances have multiple meanings.
In cases of ambiguity, models of meaning must learn that the same input can map to different meanings