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Found 22 papers, 3 papers with code
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing
The availability of corpora has led to significant advances in training semantic parsers in English.
An in-depth look at Euclidean disk embeddings for structure preserving parsing
We address this gap for two structure generation tasks, namely dependency and semantic parsing.
Revisiting text decomposition methods for NLI-based factuality scoring of summaries
In this paper we systematically compare different granularities of decomposition -- from document to sub-sentence level, and we show that the answer is no.
Visual Semantic Parsing: From Images to Abstract Meaning Representation
The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e. g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations.
Dependency parsing with structure preserving embeddings
We do so by casting dependency parsing as a tree embedding problem where we incorporate geometric properties of dependency trees in the form of training losses within a graph-based parser.
How coherent are neural models of coherence?
Despite the recent advances in coherence modelling, most such models including state-of-the-art neural ones, are evaluated on either contrived proxy tasks such as the standard order discrimination benchmark, or tasks that require special expert annotation.
Accurate polyglot semantic parsing with DAG grammars
We significantly improve upon this work, by proposing a simpler architecture as well as more efficient training and inference algorithms that can always guarantee the well-formedness of the generated graphs.
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction.
Ranked #5 on
DRS Parsing
on PMB-2.2.0
A survey of cross-lingual features for zero-shot cross-lingual semantic parsing
The availability of corpora to train semantic parsers in English has lead to significant advances in the field.
Neural Networks for Cross-lingual Negation Scope Detection
Negation scope has been annotated in several English and Chinese corpora, and highly accurate models for this task in these languages have been learned from these annotations.
Talking to myself: self-dialogues as data for conversational agents
Conversational agents are gaining popularity with the increasing ubiquity of smart devices.
Evaluating Machine Translation Performance on Chinese Idioms with a Blacklist Method
Our evaluation confirms that a sizable number of idioms in our test set are mistranslated (46. 1%), that literal translation error is a common error type, and that our blacklist method is effective at identifying literal translation errors.
Edina: Building an Open Domain Socialbot with Self-dialogues
We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition.
Universal Dependencies to Logical Form with Negation Scope
Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources.
Detecting negation scope is easy, except when it isn't
Several corpora have been annotated with negation scope{---}the set of words whose meaning is negated by a cue like the word {``}not{''}{---}leading to the development of classifiers that detect negation scope with high accuracy.
Neural Networks for Negation Cue Detection in Chinese
In particular, the use of a character-based model allows us to capture characteristics of negation cues in Chinese using word-embedding information only.
Universal Dependencies to Logical Forms with Negation Scope
Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources.
