Generalized Earley parser predicts future events from sequence data.
problem Predicting future events from unsegmented, unlabeled sequence data.
method Integrates grammar parsing and classification for optimal segmentation and labels.
result Significantly outperforms other approaches in future human activity prediction.
Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced seque…
Neural parser learns to decompose objects into segments without ground truth.
problem Learning to decompose spatial tasks into segments for robots.
method Formulated as parsing approach, learned to imitate expert using DRAG policy gradient.
result Neural parser generalizes to natural images without ground truth.
Improves semantic parsing with human feedback in a counterfactual setup.
problem Expensive gold parses or answers for training semantic parsers.
method Convert model outputs to human-understandable statements for feedback.
result Significant improvement in semantic parsing through counterfactual learning.
A method for non-projective dependency parsing without fixed edge order.
problem Non-projective dependency parsing without fixed edge order.
method Incremental edge prediction, blending graph, transition, and easy-first parsing.
result Successfully parses near state-of-the-art on projective and non-projective languages.
West Frisian lemmatizer, POS tagger, and parser created.
problem Creating accurate lemmatization, POS tagging, and dependency parsing for West Frisian.
method Using a corpus of 44,714 words annotated according to Universal Dependency version 2. Applying Dutch POS tags and morphological/syntactic annotations to create Frisian translations.
result Significant improvement in lemma accuracy compared to default parameters.
Counterfactual learning from human bandit feedback describes a scenario where user feedback on the quality of outputs of a historic system is logged and used to improve a target system. We show how to apply this learning framework to neural semantic parsing. From a machine learning perspective, the key challenge lies i…
Privacy-preserving syntactic parsing using obfuscation.
problem Preserving privacy while parsing encrypted natural language data.
method Introducing a neural model that obfuscates words in natural language texts, preserving syntactic relationships.
result The obfuscated text leads to better performance on syntactic parsers compared to a random substitution baseline.
Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct resu…
Expanding spoken language understanding to handle complex entities and intents.
problem Handling compound entities and intents in spoken language understanding.
method Introducing a domain-agnostic shallow parser that handles linguistic coordination, learning domain-independent and slot-independent features.
result The model learns to segment conjunct boundaries of various phrasal categories and improves generalization across different slot types using adversarial training.
New method learns latent structures for deep NLP models without tradeoffs.
problem Joint learning of latent structures and downstream predictors with end-to-end differentiability.
method SparseMAP inference for joint learning of latent structures and downstream predictors.
result First method to enable unrestricted dynamic computation graph construction from global latent structure while maintaining differentiability.
Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and …
In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and …
New neural KB representation speeds up reasoning with large symbolic knowledge bases.
problem Efficiently reasoning with large symbolic knowledge bases.
method Sparse-matrix reified knowledge base, enabling fully differentiable, scalable neural modules.
result Competitive performance on KB completion and semantic parsing benchmarks.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
problem Challenges in translating natural language trading intents into correct option strategies due to the complexity of option chain data.
method Introduce Option Query Language (OQL) as a domain-specific intermediate representation to abstract option markets into high-level primitives under grammatical rules. Use LLMs as semantic parsers and validate queries by an engine.
result Significantly improves execution accuracy and logical consistency over direct baselines.
Paper improves text-to-SQL translation by encoding schema relations with self-attention.
problem Improving text-to-SQL translation accuracy across diverse databases.
method Uses relation-aware self-attention to encode schema information.
result Significant gains on Spider dataset (42.94% exact match accuracy).
Unsupervised RNNGs perform similarly to supervised ones in language modeling and grammar induction.
problem Training RNNGs requires annotated data, which is costly.
method Amortized variational inference with a neural CRF parser.
result Unsupervised RNNGs achieve comparable performance to supervised ones.
Learning a natural language interface for database tables is a challenging task that involves deep language understanding and multi-step reasoning. The task is often approached by mapping natural language queries to logical forms or programs that provide the desired response when executed on the database. To our knowle…
Domain-general semantic parsing is a long-standing goal in natural language processing, where the semantic parser is capable of robustly parsing sentences from domains outside of which it was trained. Current approaches largely rely on additional supervision from new domains in order to generalize to those domains. We …
Enhanced ontology learning from text improves question-answering systems.
problem Improving ontology learning from unstructured text for better question-answering systems.
method Heuristically modified FP-Tree with DFA for concept extraction and frequent pattern mining for ontology learning.
result Our approach significantly improves question-answering system performance, answering 80% of questions compared to 28.4% with Text2Onto.
Synthetic reference strings are as effective as real ones for training citation parsing models.
problem Lack of training data for citation parsing, especially with deep neural networks.
method Trained Grobid with human-labelled and synthetically created reference strings, and evaluated retraining and out-of-sample data impact.
result Synthetic and real reference strings are equally effective for training Grobid, with retraining improving performance.
Extracts StarCraft II tournament data for AI and ML studies.
problem Lack of accessible esports data for scientific use.
method Gathered and processed StarCraft II tournament replays using an API parser library.
result The largest publicly available StarCraft II esports dataset.
AutoKE automates embedding physical knowledge into neural networks for complex engineering problems.
problem Complex physical equations in engineering problems.
method AutoKE framework using deep neural networks, equation parsing, automatic differentiation, adaptive weights, and NAS.
result Automatically embeds physical knowledge into neural networks for complex equations efficiently.