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Found 16 papers, 9 papers with code
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
DocAMR: Multi-Sentence AMR Representation and Evaluation
Despite extensive research on parsing of English sentences into Abstraction Meaning Representation (AMR) graphs, which are compared to gold graphs via the Smatch metric, full-document parsing into a unified graph representation lacks well-defined representation and evaluation.
Learning Cross-Lingual IR from an English Retriever
We present DR. DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD).
Cross-Lingual Information Retrieval
Knowledge Distillation
+3
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning.
Ranked #1 on
AMR Parsing
on Bio
(using extra training data)
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.
Ranked #8 on
AMR Parsing
on LDC2017T10
(using extra training data)
Ensembling Graph Predictions for AMR Parsing
In many machine learning tasks, models are trained to predict structure data such as graphs.
Ranked #2 on
AMR Parsing
on LDC2020T02
(using extra training data)
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
Bootstrapping Multilingual AMR with Contextual Word Alignments
We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Pushing the Limits of AMR Parsing with Self-Learning
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.
Ranked #2 on
AMR Parsing
on LDC2014T12
GPT-too: A language-model-first approach for AMR-to-text generation
Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs.
Ranked #8 on
AMR-to-Text Generation
on LDC2017T10
Nonparametric Deconvolution Models
This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors.
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.
Language Independent Dependency to Constituent Tree Conversion
We present a dependency to constituent tree conversion technique that aims to improve constituent parsing accuracies by leveraging dependency treebanks available in a wide variety in many languages.
