Papers CCG Supertagging

Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions

The syntactic categories of categorial grammar formalisms are structured units made of smaller, indivisible primitives, bound together by the underlying grammar's category formation rules. In the trending approach of con…

Something Old, Something New: Grammar-based CCG Parsing with Transformer Models

This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing appr…

CCG Supertagging as Top-down Tree Generation

Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks

Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studie…

Supertagging with CCG primitives

In CCG and other highly lexicalized grammars, supertagging a sentence{'}s words with their lexical categories is a critical step for efficient parsing. Because of the high degree of lexicalization in these grammars, the …

Hierarchically-Refined Label Attention Network for Sequence Labeling

CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This ca…

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to target the comprehension of specific type…

An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search

Globally normalized neural sequence models are considered superior to their locally normalized equivalents because they may ameliorate the effects of label bias. However, when considering high-capacity neural parametriza…

Language Modeling Teaches You More than Translation Does: Lessons Learned Through Auxiliary Syntactic Task Analysis

Recently, researchers have found that deep LSTMs trained on tasks like machine translation learn substantial syntactic and semantic information about their input sentences, including part-of-speech. These findings begin …

Semi-Supervised Sequence Modeling with Cross-View Training

Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the superv…

Targeted Syntactic Evaluation of Language Models

We present a dataset for evaluating the grammaticality of the predictions of a language model. We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical…

Linguistically Rich Vector Representations of Supertags for TAG Parsing

A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models

Beam search is a desirable choice of test-time decoding algorithm for neural sequence models because it potentially avoids search errors made by simpler greedy methods. However, typical cross entropy training procedures …

Initial Explorations of CCG Supertagging for Universal Dependency Parsing

In this paper we describe the system by METU team for universal dependency parsing of multilingual text. We use a neural network-based dependency parser that has a greedy transition approach to dependency parsing. CCG su…

Exploring the Syntactic Abilities of RNNs with Multi-task Learning

Recent work has explored the syntactic abilities of RNNs using the subject-verb agreement task, which diagnoses sensitivity to sentence structure. RNNs performed this task well in common cases, but faltered in complex se…

Keystroke dynamics as signal for shallow syntactic parsing

Keystroke dynamics have been extensively used in psycholinguistic and writing research to gain insights into cognitive processing. But do keystroke logs contain actual signal that can be used to learn better natural lang…

An Empirical Exploration of Skip Connections for Sequential Tagging

In this paper, we empirically explore the effects of various kinds of skip connections in stacked bidirectional LSTMs for sequential tagging. We investigate three kinds of skip connections connecting to LSTM cells: (a) s…

A Dynamic Window Neural Network for CCG Supertagging

Combinatory Category Grammar (CCG) supertagging is a task to assign lexical categories to each word in a sentence. Almost all previous methods use fixed context window sizes as input features. However, it is obvious that…

Deep multi-task learning with low level tasks supervised at lower layers

LSTM CCG Parsing