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Found 127 papers, 42 papers with code
Exploring Methodologies for Collecting High-Quality Implicit Reasoning in Arguments
Annotation of implicit reasoning (i. e., warrant) in arguments is a critical resource to train models in gaining deeper understanding and correct interpretation of arguments.
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension
Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems.
Exploring Methods for Generating Feedback Comments for Writing Learning
To shed light on these points, we investigate a wider range of methods for generating many feedback comments in this study.
SHAPE : Shifted Absolute Position Embedding for Transformers
Position representation is crucial for building position-aware representations in Transformers.
Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model
Ensembling is a popular method used to improve performance as a last resort.
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges.
Context Limitations Make Neural Language Models More Human-Like
In this study, we demonstrate the discrepancies in context access between modern neural language models (LMs) and humans in incremental sentence processing.
LPAttack: A Feasible Annotation Scheme for Capturing Logic Pattern of Attacks in Arguments
Attacking is not always straightforward and often comprise complex rhetorical moves such that arguers might agree with a logic of an argument while attacking another logic.
TYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation
In this paper, we define three criteria that a template set should satisfy: expressiveness, informativeness, and uniqueness, and verify the feasibility to create a template set that satisfies these criteria as a first trial.
COPA-SSE: Semi-structured Explanations for Commonsense Reasoning
We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9, 747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions.
Annotating Implicit Reasoning in Arguments with Causal Links
Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge.
Instance-Based Neural Dependency Parsing
Interpretable rationales for model predictions are crucial in practical applications.
Transformer-based Lexically Constrained Headline Generation
We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer.
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Transformer architecture has become ubiquitous in the natural language processing field.
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system.
SHAPE: Shifted Absolute Position Embedding for Transformers
Position representation is crucial for building position-aware representations in Transformers.
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics
We also find that the generalization performance to unseen combinations is better when the form of meaning representations is simpler.
Lower Perplexity is Not Always Human-Like
Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.
Learning to Learn to be Right for the Right Reasons
Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and hard test set without superficial cues.
A Comparative Study on Collecting High-Quality Implicit Reasonings at a Large-scale
We construct a preliminary dataset of 6, 000 warrants annotated over 600 arguments for 3 debatable topics.
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution
Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR).
Two Training Strategies for Improving Relation Extraction over Universal Graph
This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a large-scale text collection.
Exploring Transitivity in Neural NLI Models through Veridicality
Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear.
Efficient Estimation of Influence of a Training Instance
Understanding the influence of a training instance on a neural network model leads to improving interpretability.
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain.
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Events in a narrative differ in salience: some are more important to the story than others.
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution
This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data.
Corruption Is Not All Bad: Incorporating Discourse Structure into Pre-training via Corruption for Essay Scoring
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.
Langsmith: An Interactive Academic Text Revision System
Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English.
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets.
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries
Pretrained language models have been suggested as a possible alternative or complement to structured knowledge bases.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
We introduce a new task formulation of SAS that matches the actual usage.
Evaluation of Similarity-based Explanations
In this study, we investigated relevance metrics that can provide reasonable explanations to users.
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
In general, the labels used in sequence labeling consist of different types of elements.
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC.
Ranked #2 on
Grammatical Error Correction
on JFLEG
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
We examine a methodology using neural language models (LMs) for analyzing the word order of language.
Creating Corpora for Research in Feedback Comment Generation
In this paper, we report on datasets that we created for research in feedback comment generation {---} a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning.
Word Rotator's Distance
Accordingly, we propose a method that first decouples word vectors into their norm and direction, and then computes alignment-based similarity using earth mover's distance (i. e., optimal transport cost), which we refer to as word rotator's distance.
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?
This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness
Large-scale dialogue datasets have recently become available for training neural dialogue agents.
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Interpretable rationales for model predictions play a critical role in practical applications.
Evaluating Dialogue Generation Systems via Response Selection
Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation.
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing.
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets
Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems.
Improving Evidence Detection by Leveraging Warrants
Recognizing the implicit link between a claim and a piece of evidence (i. e. warrant) is the key to improving the performance of evidence detection.
Inject Rubrics into Short Answer Grading System
Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance.
When Choosing Plausible Alternatives, Clever Hans can be Clever
Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis
In transductive learning, an unlabeled test set is used for model training.
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
The writing process consists of several stages such as drafting, revising, editing, and proofreading.
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets.
Riposte! A Large Corpus of Counter-Arguments
Counter-arguments (CAs), one form of constructive feedback, have been proven to be useful for critical thinking skills.
Select and Attend: Towards Controllable Content Selection in Text Generation
As a result, the content to be described in the text cannot be explicitly controlled.
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Language technologies play a key role in assisting people with their writing.
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models.
Ranked #5 on
Grammatical Error Correction
on BEA-2019 (test)
Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring
This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts.
An Empirical Study of Span Representations in Argumentation Structure Parsing
For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established.
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring
Existing document embedding approaches mainly focus on capturing sequences of words in documents.
Can neural networks understand monotonicity reasoning?
Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures.
The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4
Our system achieved 80. 9{\%} accuracy on the test set for the formal run and got the 3rd place out of 42 teams.
Annotating with Pros and Cons of Technologies in Computer Science Papers
Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study.
Subword-based Compact Reconstruction of Word Embeddings
The idea of subword-based word embeddings has been proposed in the literature, mainly for solving the out-of-vocabulary (OOV) word problem observed in standard word-based word embeddings.
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset.
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena.
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?
This study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models.
A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation
However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths.
Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models
Developing a method for understanding the inner workings of black-box neural methods is an important research endeavor.
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates
Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e. g. attack-support relations, stance, explicit premise/claim).
Suspicious News Detection Using Micro Blog Text
We present a new task, suspicious news detection using micro blog text.
Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning Framework
We also demonstrate that our method has the more data, better performance property with promising scalability to the amount of unlabeled data.
Investigating the Challenges of Temporal Relation Extraction from Clinical Text
Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain.
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
As well as deriving PMI from mutual information, we derive this new measure from the Hilbert--Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC).
What Makes Reading Comprehension Questions Easier?
From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions.
Predicting Stances from Social Media Posts using Factorization Machines
This paper presents an approach to detect the stance of a user toward a topic based on their stances toward other topics and the social media posts of the user.
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis
Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese.
A Melody-Conditioned Lyrics Language Model
This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody.
Cross-Lingual Learning-to-Rank with Shared Representations
Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query.
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base.
Unsupervised Learning of Style-sensitive Word Vectors
This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner.
Source-side Prediction for Neural Headline Generation
The encoder-decoder model is widely used in natural language generation tasks.
A Corpus of Deep Argumentative Structures as an Explanation to Argumentative Relations
Our coverage result of 74. 6% indicates that argumentative relations can reasonably be explained by our small pattern set.
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems
In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage.
Generating Stylistically Consistent Dialog Responses with Transfer Learning
We propose a novel, data-driven, and stylistically consistent dialog response generation system.
Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction
This paper explores the idea of robot editors, automated proofreaders that enable journalists to improve the quality of their articles.
Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions.
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse
This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models.
Analyzing the Revision Logs of a Japanese Newspaper for Article Quality Assessment
We address the issue of the quality of journalism and analyze daily article revision logs from a Japanese newspaper company.
Composing Distributed Representations of Relational Patterns
Learning distributed representations for relation instances is a central technique in downstream NLP applications.
Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization
We present in this paper our approach for modeling inter-topic preferences of Twitter users: for example, those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade.
Modeling Discourse Segments in Lyrics Using Repeated Patterns
This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics.
Modeling Context-sensitive Selectional Preference with Distributed Representations
This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP).
Learning Semantically and Additively Compositional Distributional Representations
This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS).
Neural Architectures for Fine-grained Entity Type Classification
In this work, we investigate several neural network architectures for fine-grained entity type classification.
Question-Answering with Logic Specific to Video Games
We chose a popular game called {`}Minecraft{'}, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database.
An Attentive Neural Architecture for Fine-grained Entity Type Classification
In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts.
The Mechanism of Additive Composition
Additive composition (Foltz et al, 1998; Landauer and Dumais, 1997; Mitchell and Lapata, 2010) is a widely used method for computing meanings of phrases, which takes the average of vector representations of the constituent words.
