Unsupervised method parses instructional videos into semantic steps.
problem Parsing instructional videos into semantic steps without supervision.
method Joint visual and language model to identify steps and provide descriptions.
result Method discovers semantically correct instructions for various tasks.
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.
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.
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…
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.
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.
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.
Generative model improves semantic parsing across domains.
problem Robust semantic parsing across multiple domains.
method Probabilistic generative grammar with hierarchical Dirichlet processes.
result Efficient algorithms for training, parsing, and sentence generation.
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.
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.
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). 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.
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.
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.
Ground-A-Video edits videos without training, preserving intended changes.
problem Complex multi-attribute video editing with omitted or wrong changes.
method Grounding-guided video-to-video translation with Cross-Frame Gated Attention.
result Zero-shot multi-attribute video editing with improved accuracy and frame consistency.
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.
Generates, predicts, and completes human action videos with a two-stage deep framework.
problem Severe ill-posedness in video generation, prediction, and completion.
method Two-stage deep framework: 1) Generates human pose sequence from noise, 2) Converts pose sequence to video.
result Produces high-quality video generation/prediction/completion results of longer duration.
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.
CB-GLNs learn video data's complex dependencies via graph representation.
problem Capturing complex dependency structures in sequential data like videos.
method Represent video data as a graph, find compositional dependencies via graph-cut and message passing.
result CB-GLNs efficiently learn video data's semantic compositional structure.
RaMViD uses diffusion models for video prediction and infilling.
problem Predicting and infilling missing information in videos.
method Extends image diffusion models to videos using 3D convolutions and a new conditioning technique.
result Achieves state-of-the-art results on video prediction benchmarks.
New metric for video generation models, addressing quality, coherence, and diversity.
problem Lack of metrics for evaluating generative models of video.
method Proposed Fréchet Video Distance (FVD) and StarCraft 2 Videos (SCV) benchmark.
result FVD correlates well with human judgment of generated videos.
LumièreNet creates lecture videos from audio narration.
problem Creating high-quality lecture videos from audio narration.
method Modular deep-learning architecture that learns from audio to video.
result Synthesizes high-quality lecture videos from any length of audio.
Paper defends against adversarial videos by detecting and reducing imperceptible perturbations.
problem Adversarial videos can fool well-trained video classification models.
method Temporal consistency between frames and spatial denoising to detect and reduce perturbations.
result The proposed method significantly improves robustness against adversarial attacks.
Paper improves video categorization using temporal coherence.
problem Video categorization in multiple modalities.
method Temporal coherence-based regularization for multimodal models.
result Models with temporal coherence outperform state-of-the-art.
Improves video search by balancing text and visual modalities.
problem Modality imbalance in video search models, focusing mainly on text matching.
method Proposes MBVR with MS samples and DM to balance modalities.
result Empirically shows significant improvement in modality balance and search effectiveness.
A new video prediction model treats videos as continuous processes, reducing sampling steps and improving efficiency.
problem Efficiency and temporal coherence in video prediction models.
method Treats videos as a continuous multi-dimensional process, reducing sampling steps.
result Reduction of 75% sampling steps, state-of-the-art performance on benchmark datasets.
UDVD uses deep learning to denoise videos without supervision.
problem Lack of clean video data for training deep learning models.
method UDVD is a CNN trained solely on noisy video data, adapting to local motion.
result UDVD performs as well as supervised methods, even with limited training data.
Paper predicts video popularity from web traces, not hosting services.
problem Predicting video popularity from web traces.
method Combining embed/link data and internal search logs to predict video popularity.
result Embed and link data can significantly improve video popularity prediction.
Generative model for high-resolution video generation.
problem Challenges in generating high-resolution videos due to memory and training stability limitations.
method Progressive growing of sliced Wasserstein GANs (SWGAN) for incremental spatiotemporal information learning.
result Generated photorealistic face videos of 256x256x32 resolution with an inception score of 14.57.
Proposes TDNs for learning complex video structures.
problem Complex temporal dependencies in sequential data, especially videos.
method Temporal Dependency Networks (TDNs) using graph representations and graph convolutions.
result Efficiently learns complex semantic structures of video data.