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Found 27 papers, 12 papers with code
Market Comment Generation from Data with Noisy Alignments
On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data.
Generating Racing Game Commentary from Vision, Language, and Structured Data
In this study, we introduce a new large-scale dataset that contains aligned video data, structured numerical data, and transcribed commentaries that consist of 129, 226 utterances in 1, 389 races in a game.
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion
On average, the performance of our best model represents a 19 \% increase in accuracy over the worst choice across all languages.
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like.
Effective Batching for Recurrent Neural Network Grammars
However, RNNGs are known to be harder to scale due to the difficulty of batched training.
An empirical analysis of existing systems and datasets toward general simple question answering
Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering.
Learning with Contrastive Examples for Data-to-Text Generation
Existing models for data-to-text tasks generate fluent but sometimes incorrect sentences e. g., {``}Nikkei gains{''} is generated when {``}Nikkei drops{''} is expected.
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers.
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An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models
Neural language models are commonly trained only on positive examples, a set of sentences in the training data, but recent studies suggest that the models trained in this way are not capable of robustly handling complex syntactic constructions, such as long-distance agreement.
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels
We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model.
Learning to Select, Track, and Generate for Data-to-Text
We propose a data-to-text generation model with two modules, one for tracking and the other for text generation.
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees.
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data.
Dynamic Feature Selection with Attention in Incremental Parsing
One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context.
An Empirical Investigation of Error Types in Vietnamese Parsing
Syntactic parsing plays a crucial role in improving the quality of natural language processing tasks.
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas.
Can Discourse Relations be Identified Incrementally?
Humans process language word by word and construct partial linguistic structures on the fly before the end of the sentence is perceived.
Effective Online Reordering with Arc-Eager Transitions
We present a new transition system with word reordering for unrestricted non-projective dependency parsing.
Adversarial Training for Cross-Domain Universal Dependency Parsing
We describe our submission to the CoNLL 2017 shared task, which exploits the shared common knowledge of a language across different domains via a domain adaptation technique.
A* CCG Parsing with a Supertag and Dependency Factored Model
Our model achieves the state-of-the-art results on English and Japanese CCG parsing.
Multilingual Back-and-Forth Conversion between Content and Function Head for Easy Dependency Parsing
Universal Dependencies (UD) is becoming a standard annotation scheme cross-linguistically, but it is argued that this scheme centering on content words is harder to parse than the conventional one centering on function words.
Left-corner Methods for Syntactic Modeling with Universal Structural Constraints
This connection suggests left-corner methods can be a tool to exploit the universal syntactic constraint that people avoid generating center-embedded structures.
