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.
Neural model parses non-projective dependency trees efficiently.
problem Parsing non-projective dependency trees.
method Probabilistic parsing model using neural representations and Kirchhoff's Matrix-Tree Theorem.
result State-of-the-art parsing performance on nine datasets.
In Finsler geometry, there are infinitely many models of constant curvature. The Funk metrics, the Hilbert-Klein metrics and the Bryant metrics are projectively flat with non-zero constant curvature. A recent example constructed by the author is projectively flat with zero curvature. In this paper, we introduce a techn…
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.
This paper explores unsupervised learning of parsing models along two directions. First, which models are identifiable from infinite data? We use a general technique for numerically checking identifiability based on the rank of a Jacobian matrix, and apply it to several standard constituency and dependency parsing mode…
Paper proposes a reinforcement learning framework for distant supervision of question parsing.
problem Lack of gold annotations for parsing in QA datasets.
method Reinforcement learning framework based on formal queries to learn mentions of entities and relations.
result Significant improvement in entity and relation linking compared to state of the art.
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.
Flexible log file parsing using HMM adapts to evolving content.
problem Dynamic log file processing with evolving content.
method Modeling frequent patterns into HMM for flexible log file parsing.
result High accuracy (over 99%) in parsing different system log files.
Abstract Syntax Networks generate code and parse text with high accuracy.
problem Mapping unstructured inputs to well-formed outputs for code generation and semantic parsing.
method Dynamic AST construction using a decoder with a modular structure.
result 79.2 BLEU and 22.7% exact match accuracy on Hearthstone dataset.
DocParser parses document structures from renderings like PDFs and scans.
problem Parsing complete hierarchical document structures from renderings.
method End-to-end system with novel weak supervision approach.
result Significant improvement in document structure parsing performance.
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 …
Graph-to-Tree Neural Networks improve structured input-output translation in tasks like semantic parsing and math word problems.
problem Improving performance on tasks like semantic parsing and math word problem solving.
method Graph-to-Tree Neural Networks, consisting of a graph encoder and a hierarchical tree decoder.
result Graph2Tree model outperforms or matches state-of-the-art models on neural semantic parsing and math word problem tasks.
Deep models learn to parse complex language structures from local data patterns.
problem Understanding how deep models parse and represent language structures.
method Introduced tunable probabilistic context-free grammars and a learning algorithm inspired by deep networks.
result Data correlations across scales enable hierarchical language representations.
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.
By computing certain cohomology of Vect(M) of smooth vector fields we prove that on 1-dimensional manifolds M there is no quantization map intertwining the action of non-projective embeddings of the Lie algebra sl(2) into the Lie algebra Vect(M). Contrariwise, for projective embeddings sl(2)-equivariant quantization ex…
Paper proposes zero-shot transfer learning for semantic parsing.
problem Applying neural networks to tasks with little data remains challenging.
method Introduces a new method for learning shared space between domains based on domain label prediction.
result Method outperforms state-of-the-art techniques in zero-shot experimental setting.
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.
Co-PLNet combines point and line predictions to improve wireframe parsing accuracy and efficiency.
problem Separate line and point predictions lead to inconsistent wireframes.
method Co-PLNet uses a Point-Line Prompt Encoder to convert early point detections into spatial prompts, which guide line refinement.
result Co-PLNet achieves better accuracy and robustness in wireframe parsing compared to existing methods.
Proposes a method to interpret linguistic data models using parse trees and least-squares scores.
problem Interpreting trained classification models in linguistic data sets.
method Assigns least-squares based importance scores to words in a sentence using syntactic constituency structure and relates them to the Banzhaf value in coalitional game theory.
result Demonstrates the effectiveness of the proposed method in aiding interpretability and diagnostics for language models.
Improves neural semantic parsers using human feedback.
problem Improving neural semantic parsers through human feedback.
method Counterfactual learning from human bandit feedback, reweighting estimator, stochastic gradient optimization.
result Significant improvement in semantic parsers achieved.
PATOIS synthesizes code from natural language using learned code idioms.
problem Synthesizing general-purpose source code from natural language specifications is challenging.
method PATOIS uses a neural synthesizer that interleaves high-level and low-level reasoning, incorporating learned code idioms from a corpus.
result Using learned code idioms improves the synthesizer's accuracy on semantic parsing datasets.
Survey on automating geometry problem solving with large models.
problem Automating geometric problem solving with spatial understanding and logical reasoning.
method Synthesizes GPS advancements through benchmark construction, parsing, and reasoning paradigms.
result Unified analytical paradigm and emerging opportunities identified.
Unsupervised algorithm parses CSG images into CFG without pretraining.
problem Sparse reward problem in unsupervised program synthesis for images.
method Grammar-encoded tree LSTM, entropy regularization, sampling without replacement.
result Recover meaningful programs in large search spaces (up to 3.8imes1028). Alternative proof shows Kummer rigidity for K3 surfaces.
problem Proving automorphisms of K3 surfaces are Kummer examples.
method Exploits Ricci-flat metrics on K3 surfaces.
result Shows measure of maximal entropy automorphisms are Kummer.
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.
Automatically mined rules from dependency parsing help neural models learn from less labeled data.
problem Lack of labeled data for aspect and opinion term extraction.
method Automatically mined rules from dependency parsing, applied to auxiliary data, combined with human-annotated data.
result Neural models achieve better performance than state-of-the-art with mined rules and auxiliary data.
We give an explicit description of rational curves in the product of three copies of complex projective lines, which are transformed into twistor lines in M. Nagata's example of non-projective complete algebraic variety, viewed as the twistor space of Eguchi-Hanson metric. In particular, we show that there exist two fa…
Novel ramp loss method improves weakly supervised machine translation and parsing.
problem Training neural models without gold labels in weak supervision scenarios.
method Adapted ramp loss objectives to promote positive outputs and discourage negative ones.
result Bipolar ramp loss objectives outperform other methods on weakly supervised tasks.
Scene parsing is an important and challenging prob- lem in computer vision. It requires labeling each pixel in an image with the category it belongs to. Tradition- ally, it has been approached with hand-engineered features from color information in images. Recently convolutional neural networks (CNNs), which automatica…
Bayesian framework refines scene interpretation with prior and posterior models.
problem Challenges in incorporating contextual relationships into scene recognition.
method Combines prior scene geometry and object spatial arrangement with classifier outputs.
result Progressive refinement of scene interpretation through mutual information maximization.
In this paper, we classify the spherically symmetric Berwald metrics in Rn. For the spherically symmetric Landsberg metrics, we prove that there do not exist any non-Berwald metrics among the regular case. The partial differential equation systems which can respectively characterize the spherically symmetri…
Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications focused on only one task and not much work explored this framework for multiple tasks. This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the onet…
Framework for universal graph function approximators outperforms existing methods.
problem Graph classification and separation of graph classes.
method Inspired by persistent homology, dependency parsing, and multivalued functions, the framework constructs universal approximators on graph isomorphism classes.
result Achieves state-of-the-art performance on four graph datasets.
We introduce a cohomological obstruction to solving the constant scalar curvature Kähler (cscK) equation twisted by a semipositive form, appearing in works of Fine and Song-Tian. Geometrically this gives an obstruction for a manifold to be the base of a holomorphic submersion carrying a cscK metric in certain ``adiabat…
Kähler submanifolds in Iwasawa manifolds are studied.
problem Characterizing Kähler submanifolds in Iwasawa manifolds.
method Analyzing quotient groups and using complex homogeneity.
result Kähler surfaces in Iwasawa manifolds are either abelian or non-projective isotrivial elliptic.
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…
MAPO uses a memory buffer to improve policy optimization in structured prediction tasks.
problem Improving sample efficiency and robustness in policy optimization for structured prediction tasks.
method Memory Augmented Policy Optimization (MAPO) uses a memory buffer to reduce policy gradient variance.
result MAPO achieves state-of-the-art results in program synthesis and semantic parsing tasks.
New grammar model learns sentence structure with latent variables.
problem Grammar induction for sentences with complex dependencies.
method Compound probabilistic context-free grammar with latent variables, variational inference.
result Effective unsupervised parsing compared to state-of-the-art methods.
SparseMAP selects sparse structures efficiently for structured prediction.
problem Efficiently searching over combinatorial structures in structured prediction.
method SparseMAP: a new method for sparse structured inference with a differentiable loss function.
result SparseMAP selects only a few global structures efficiently.
Neurally-Guided Structure Inference combines search and data-driven methods for efficient, robust structure inference.
problem Combining the advantages of exhaustive search and data-driven methods for structure inference.
method Neurally-Guided Structure Inference (NG-SI) uses a neural network to guide hierarchical search over structures.
result NG-SI outperforms search-based and data-driven methods on probabilistic matrix decomposition and symbolic program parsing.
New meta-learning method outperforms human-designed architectures in dense image prediction tasks.
problem Designing efficient neural network architectures for dense image prediction.
method Recursive search space construction for multi-scale visual information.
result Meta-learning method achieves state-of-the-art performance on scene parsing, person-part segmentation, and semantic image segmentation.
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.
The paper sets a lower bound on the crossing number of 2-bridge knots and answers a question about their epimorphism number.
problem Determining the epimorphism number of 2-bridge knots and answering a specific question posed by Suzuki.
method Using techniques related to continued fraction expansions and parsings of 2-bridge knots.
result Established a lower bound on the crossing number of 2-bridge knots and answered Suzuki's question about the epimorphism number.
Generative model ensures valid discrete data outputs.
problem Challenges in generative modeling of discrete data like arithmetic expressions and molecular structures.
method Grammar Variational Autoencoder (GVAE) that encodes and decodes to/from parse trees, ensuring valid outputs.
result Generates more coherent latent space with valid discrete outputs.
We speed up marginal inference by ignoring factors that do not significantly contribute to overall accuracy. In order to pick a suitable subset of factors to ignore, we propose three schemes: minimizing the number of model factors under a bound on the KL divergence between pruned and full models; minimizing the KL dive…
Simple framework decouples word alignment and multilingual embedding mapping.
problem Learning multilingual embeddings without supervision.
method Two-stage approach: 1) unsupervised word alignment, 2) mapping embeddings to shared space.
result Robust performance across various multilingual tasks, including distant languages.
The abstract proves a Moser-like theorem for C-symplectic structures and applies it to complex manifolds.
problem Analyzing the isotopy of C-symplectic structures and their applications.
method Proves an analogue of Moser's isotopy theorem for families of C-symplectic structures.
result Locally trivial degenerate twistorial deformation over the base of holomorphic Lagrangian fibrations.
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.