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Found 177 papers, 67 papers with code
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.
IRAC: A Domain-Specific Annotated Corpus of Implicit Reasoning in Arguments
To solve this problem, we hypothesize that as human reasoning is guided by innate collection of domain-specific knowledge, it might be beneficial to create such a domain-specific corpus for machines.
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.
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling
Semantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates.
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.
SHAPE : Shifted Absolute Position Embedding for Transformers
Position representation is crucial for building position-aware representations in Transformers.
Emergence of Primacy and Recency Effect in Mamba: A Mechanistic Point of View
We study memory in state-space language models using primacy and recency effects as behavioral tools to uncover how information is retained and forgotten over time.
Spelling-out is not Straightforward: LLMs' Capability of Tokenization from Token to Characters
Large language models (LLMs) can spell out tokens character by character with high accuracy, yet they struggle with more complex character-level tasks, such as identifying compositional subcomponents within tokens.
On Entity Identification in Language Models
We analyze the extent to which internal representations of language models (LMs) identify and distinguish mentions of named entities, focusing on the many-to-many correspondence between entities and their mentions.
Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge
Fact recall, the ability of language models (LMs) to retrieve specific factual knowledge, remains a challenging task despite their impressive general capabilities.
Mechanistic Insights into Grokking from the Embedding Layer
Grokking, a delayed generalization in neural networks after perfect training performance, has been observed in Transformers and MLPs, but the components driving it remain underexplored.
SPIRIT: Patching Speech Language Models against Jailbreak Attacks
Speech Language Models (SLMs) enable natural interactions via spoken instructions, which more effectively capture user intent by detecting nuances in speech.
Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted
To evaluate the validity of the generated MCQs, Item Response Theory (IRT) was applied to compare item characteristics between MCQs generated by AnaQuest, a baseline ChatGPT prompt, and human-crafted items.
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided Distillation
Efficient path planning in robotics, particularly within large-scale, dynamic environments, remains a significant hurdle.
How LLMs Learn: Tracing Internal Representations with Sparse Autoencoders
Large Language Models (LLMs) demonstrate remarkable multilingual capabilities and broad knowledge.
Syntactic Learnability of Echo State Neural Language Models at Scale
What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability?
Rectifying Belief Space via Unlearning to Harness LLMs' Reasoning
Large language models (LLMs) can exhibit advanced reasoning yet still generate incorrect answers.
Number Representations in LLMs: A Computational Parallel to Human Perception
Humans are believed to perceive numbers on a logarithmic mental number line, where smaller values are represented with greater resolution than larger ones.
Automatic Feedback Generation for Short Answer Questions using Answer Diagnostic Graphs
We propose an "answer diagnosis graph," integrating the text's logical structure with feedback templates.
Weight-based Analysis of Detokenization in Language Models: Understanding the First Stage of Inference Without Inference
According to the stages-of-inference hypothesis, early layers of language models map their subword-tokenized input, which does not necessarily correspond to a linguistically meaningful segmentation, to more meaningful representations that form the model's ``inner vocabulary''.
RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles
We introduce the concept of the self-referencing causal cycle (abbreviated RECALL) - a mechanism that enables large language models (LLMs) to bypass the limitations of unidirectional causality, which underlies a phenomenon known as the reversal curse.
Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in Multi-Step Arithmetic Reasoning
This study investigates the internal reasoning process of language models during arithmetic multi-step reasoning, motivated by the question of when they internally form their answers during reasoning.
The Geometry of Numerical Reasoning: Language Models Compare Numeric Properties in Linear Subspaces
This paper investigates whether large language models (LLMs) utilize numerical attributes encoded in a low-dimensional subspace of the embedding space when answering logical comparison questions (e. g., Was Cristiano born before Messi?).
Repetition Neurons: How Do Language Models Produce Repetitions?
This paper introduces repetition neurons, regarded as skill neurons responsible for the repetition problem in text generation tasks.
Representational Analysis of Binding in Language Models
Moreover, we also discover the causal effect of OI on binding that when editing representations along the OI encoding direction, LMs tend to bind a given entity to other attributes accordingly.
MQM-Chat: Multidimensional Quality Metrics for Chat Translation
The complexities of chats pose significant challenges for machine translation models.
An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication
Machine translation models are still inappropriate for translating chats, despite the popularity of translation software and plug-in applications.
Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring
Specifically, given that scoring rubrics and reference answers differ for each prompt, we utilize key phrases, or representative expressions that the answer should contain to increase scores, and train a SAS model to learn the relationship between key phrases and answers using already annotated prompts (i. e., cross-prompts).
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
Multi-step reasoning instruction, such as chain-of-thought prompting, is widely adopted to explore better language models (LMs) performance.
Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling
Prior research in computational argumentation has mainly focused on scoring the quality of arguments, with less attention on explicating logical errors.
The Curse of Popularity: Popular Entities have Catastrophic Side Effects when Deleting Knowledge from Language Models
This study focuses on the knowledge stored in LMs and analyzes the relationship between the side effects of knowledge deletion and the entities related to the knowledge.
ACORN: Aspect-wise Commonsense Reasoning Explanation Evaluation
However, their correlation with majority-voted human ratings varied across different quality aspects, indicating that they are not a complete replacement.
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages.
A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems
In this paper, we build a large dataset of response generation models' contradictions for the first time.
Monotonic Representation of Numeric Properties in Language Models
Language models (LMs) can express factual knowledge involving numeric properties such as Karl Popper was born in 1902.
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading
Using this dataset, we demonstrate the performance of baselines including finetuned BERT and GPT models with few-shot in-context learning.
J-UniMorph: Japanese Morphological Annotation through the Universal Feature Schema
We introduce a Japanese Morphology dataset, J-UniMorph, developed based on the UniMorph feature schema.
Test-time Augmentation for Factual Probing
Factual probing is a method that uses prompts to test if a language model "knows" certain world knowledge facts.
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words
The performance of sentence encoders can be significantly improved through the simple practice of fine-tuning using contrastive loss.
Chat Translation Error Detection for Assisting Cross-lingual Communications
In this paper, we describe the development of a communication support system that detects erroneous translations to facilitate crosslingual communications due to the limitations of current machine chat translation methods.
Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation
The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process.
Transformer Language Models Handle Word Frequency in Prediction Head
Prediction head is a crucial component of Transformer language models.
Prompting for explanations improves Adversarial NLI. Is this true? {Yes} it is {true} because {it weakens superficial cues}
Explanation prompts ask language models to not only assign a particular label to a giveninput, such as true, entailment, or contradiction in the case of natural language inference but also to generate a free-text explanation that supports this label.
Ranked #1 on
Natural Language Inference
on ANLI test
Adversarial Natural Language Inference
Natural Language Inference
Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction
GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks.
Empirical Investigation of Neural Symbolic Reasoning Strategies
Neural reasoning accuracy improves when generating intermediate reasoning steps.
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Compositionality is a pivotal property of symbolic reasoning.
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
Transformers are ubiquitous in wide tasks.
Tracing and Manipulating Intermediate Values in Neural Math Problem Solvers
Previous research has shown that information about intermediate values of these inputs can be extracted from the activations of the models, but it is unclear where that information is encoded and whether that information is indeed used during inference.
Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction
They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attention, only share information in one direction.
Target-Guided Open-Domain Conversation Planning
Prior studies addressing target-oriented conversational tasks lack a crucial notion that has been intensively studied in the context of goal-oriented artificial intelligence agents, namely, planning.
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models
In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list.
RealTime QA: What's the Answer Right Now?
We introduce REALTIME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version).
Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring
Towards guaranteeing high-quality predictions, we present the first study of exploring the use of human-in-the-loop framework for minimizing the grading cost while guaranteeing the grading quality by allowing a SAS model to share the grading task with a human grader.
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
Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading.
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 of creating 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.
Evaluating Dialogue Generation Systems via Response Selection
Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation.
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.
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.
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.
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.
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 #14 on
Grammatical Error Correction
on CoNLL-2014 Shared Task
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.
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.
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.
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.
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.
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.
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.
A Melody-Conditioned Lyrics Language Model
This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody.
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.
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.
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.
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 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).
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.
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.
