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Found 62 papers, 35 papers with code
Putting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.
Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Abstractive Text Summarization
Natural Language Inference
+2
PreQuEL: Quality Estimation of Machine Translation Outputs in Advance
We show that this augmentation method can improve the performance of the Quality-Estimation task as well.
A Computational Acquisition Model for Multimodal Word Categorization
Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on cross-modal signals.
Some Grammatical Errors are Frequent, Others are Important
In Grammatical Error Correction, systems are evaluated by the number of errors they correct.
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
Huge language models (LMs) have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks.
Semantics-aware Attention Improves Neural Machine Translation
We further show an additional gain when using both semantic and syntactic structures in some language pairs.
On the Relation between Syntactic Divergence and Zero-Shot Performance
We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting.
Cross-lingual zero-shot dependency parsing
Relation Classification
On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation
To gain insight into the role neurons play, we study the activation patterns corresponding to meaning preserving paraphrases (e. g., active-passive).
Putting Words in BERT's Mouth: Navigating Contextualized Vector Spaces with Pseudowords
We present a method for exploring regions around individual points in a contextualized vector space (particularly, BERT space), as a way to investigate how these regions correspond to word senses.
Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech
We compile two CDS corpora with sentential logical forms, one in English and the other in Hebrew.
The Grammar-Learning Trajectories of Neural Language Models
These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena.
Part of Speech and Universal Dependency effects on English Arabic Machine Translation
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages.
$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Mediators in Determining what Processing BERT Performs First
Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks.
SERRANT: a syntactic classifier for English Grammatical Error Types
SERRANT is a system and code for automatic classification of English grammatical errors that combines SErCl and ERRANT.
Enhancing the Transformer Decoder with Transition-based Syntax
Notwithstanding recent advances, syntactic generalization remains a challenge for text decoders.
UCCA's Foundational Layer: Annotation Guidelines v2.1
This is the annotation manual for Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013), specifically the Foundational Layer.
Cross-lingual Semantic Representation for NLP with UCCA
This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew.
Semantic Structural Decomposition for Neural Machine Translation
Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation.
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other.
MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.
Classifying Syntactic Errors in Learner Language
We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence.
PMI-Masking: Principled masking of correlated spans
Specifically, we show experimentally that PMI-Masking reaches the performance of prior masking approaches in half the training time, and consistently improves performance at the end of training.
Machine Reading of Historical Events
Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities.
Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences
The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer.
Language (Re)modelling: Towards Embodied Language Understanding
While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.
MRP 2019: Cross-Framework Meaning Representation Parsing
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks.
Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation
We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model.
Made for Each Other: Broad-coverage Semantic Structures Meet Preposition Supersenses
Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013) is a typologically-informed, broad-coverage semantic annotation scheme that describes coarse-grained predicate-argument structure but currently lacks semantic roles.
Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation
We show that the state of the art Transformer Machine Translation (MT) model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model.
Preparing SNACS for Subjects and Objects
Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens.
On the Weaknesses of Reinforcement Learning for Neural Machine Translation
Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN).
Semantically Constrained Multilayer Annotation: The Case of Coreference
We propose a coreference annotation scheme as a layer on top of the Universal Conceptual Cognitive Annotation foundational layer, treating units in predicate-argument structure as a basis for entity and event mentions.
Content Differences in Syntactic and Semantic Representation
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
The Language of Legal and Illegal Activity on the Darknet
The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity.
Content Differences in Syntactic and Semantic Representations
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results.
BLEU is Not Suitable for the Evaluation of Text Simplification
BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS).
Simple and Effective Text Simplification Using Semantic and Neural Methods
Here we present a simple and efficient splitting algorithm based on an automatic semantic parser.
Ranked #18 on
Text Simplification
on TurkCorpus
Semantic Structural Evaluation for Text Simplification
Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects.
Universal Dependency Parsing with a General Transition-Based DAG Parser
This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task.
Inherent Biases in Reference-based Evaluation for Grammatical Error Correction
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB).
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general.
Comprehensive Supersense Disambiguation of English Prepositions and Possessives
Semantic relations are often signaled with prepositional or possessive marking--but extreme polysemy bedevils their analysis and automatic interpretation.
Multitask Parsing Across Semantic Representations
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others.
Ranked #3 on
UCCA Parsing
on SemEval 2019 Task 1
Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB).
Automatic Metric Validation for Grammatical Error Correction
Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings.
Reference-less Measure of Faithfulness for Grammatical Error Correction
We propose USim, a semantic measure for Grammatical Error Correction (GEC) that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output's grammaticality.
The State of the Art in Semantic Representation
Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e. g., AMR, UCCA, GMB, UDS) have been put forth.
Adposition and Case Supersenses v2.6: Guidelines for English
This document offers a detailed linguistic description of SNACS (Semantic Network of Adposition and Case Supersenses; Schneider et al., 2018), an inventory of 52 semantic labels ("supersenses") that characterize the use of adpositions and case markers at a somewhat coarse level of granularity, as demonstrated in the STREUSLE corpus (https://github. com/nert-nlp/streusle/; version 4. 5 tracks guidelines version 2. 6).
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.
Ranked #4 on
UCCA Parsing
on SemEval 2019 Task 1
HUME: Human UCCA-Based Evaluation of Machine Translation
Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales.
