Keisuke Sakaguchi


2023

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ELQA: A Corpus of Metalinguistic Questions and Answers about English
Shabnam Behzad | Keisuke Sakaguchi | Nathan Schneider | Amir Zeldes
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present ELQA, a corpus of questions and answers in and about the English language. Collected from two online forums, the >70k questions (from English learners and others) cover wide-ranging topics including grammar, meaning, fluency, and etymology. The answers include descriptions of general properties of English vocabulary and grammar as well as explanations about specific (correct and incorrect) usage examples. Unlike most NLP datasets, this corpus is metalinguistic—it consists of language about language. As such, it can facilitate investigations of the metalinguistic capabilities of NLU models, as well as educational applications in the language learning domain. To study this, we define a free-form question answering task on our dataset and conduct evaluations on multiple LLMs (Large Language Models) to analyze their capacity to generate metalinguistic answers.

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I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Chandra Bhagavatula | Jena D. Hwang | Doug Downey | Ronan Le Bras | Ximing Lu | Lianhui Qin | Keisuke Sakaguchi | Swabha Swayamdipta | Peter West | Yejin Choi
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Commonsense capabilities of pre-trained language models dramatically improve with scale, leading many to believe that scale is the only winning recipe. But is it? Here, we investigate an alternative that a priori seems impossible: can smaller language models (e.g., GPT-2) win over models that are orders of magnitude larger and better (e.g., GPT-3), if powered with novel commonsense distillation algorithms?The key intellectual challenge is to design a learning algorithm that achieve a competitive level of commonsense acquisition, without relying on the benefits of scale. In particular, we study generative models of commonsense knowledge, focusing on the task of generating generics, statements of commonsense facts about everyday concepts, e.g., birds can fly. We introduce I2D2, a novel commonsense distillation framework that loosely follows the Symbolic Knowledge Distillation of West et al. but breaks the dependence on the extreme-scale teacher model with two innovations: (1) the novel adaptation of NeuroLogic Decoding to enhance the generation quality of the weak, off-the-shelf language models, and (2) self-imitation learning to iteratively learn from the model’s own enhanced commonsense acquisition capabilities. Empirical results suggest that scale is not the only way, as novel algorithms can be a promising alternative. Moreover, our study leads to a new corpus of generics, Gen-A-tomic, that is the largest and highest quality available to date.

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Empirical Investigation of Neural Symbolic Reasoning Strategies
Yoichi Aoki | Keito Kudo | Tatsuki Kuribayashi | Ana Brassard | Masashi Yoshikawa | Keisuke Sakaguchi | Kentaro Inui
Findings of the Association for Computational Linguistics: EACL 2023

Neural reasoning accuracy improves when generating intermediate reasoning steps. However, the source of this improvement is yet unclear. Here, we investigate and factorize the benefit of generating intermediate steps for symbolic reasoning. Specifically, we decompose the reasoning strategy w.r.t. step granularity and chaining strategy. With a purely symbolic numerical reasoning dataset (e.g., A=1, B=3, C=A+3, C?), we found that the choice of reasoning strategies significantly affects the performance, with the gap becoming even larger as the extrapolation length becomes longer. Surprisingly, we also found that certain configurations lead to nearly perfect performance, even in the case of length extrapolation. Our results indicate the importance of further exploring effective strategies for neural reasoning models.

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Test-time Augmentation for Factual Probing
Go Kamoda | Benjamin Heinzerling | Keisuke Sakaguchi | Kentaro Inui
Findings of the Association for Computational Linguistics: EMNLP 2023

Factual probing is a method that uses prompts to test if a language model “knows” certain world knowledge facts. A problem in factual probing is that small changes to the prompt can lead to large changes in model output. Previous work aimed to alleviate this problem by optimizing prompts via text mining or fine-tuning. However, such approaches are relation-specific and do not generalize to unseen relation types. Here, we propose to use test-time augmentation (TTA) as a relation-agnostic method for reducing sensitivity to prompt variations by automatically augmenting and ensembling prompts at test time. Experiments show improved model calibration, i.e., with TTA, model confidence better reflects prediction accuracy. Improvements in prediction accuracy are observed for some models, but for other models, TTA leads to degradation. Error analysis identifies the difficulty of producing high-quality prompt variations as the main challenge for TTA.

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Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Keito Kudo | Yoichi Aoki | Tatsuki Kuribayashi | Ana Brassard | Masashi Yoshikawa | Keisuke Sakaguchi | Kentaro Inui
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks. This study empirically addresses this question by systematically examining recently published pre-trained seq2seq models with a carefully controlled dataset of multi-hop arithmetic symbolic reasoning. We introduce a skill tree on compositionality in arithmetic symbolic reasoning that defines the hierarchical levels of complexity along with three compositionality dimensions: systematicity, productivity, and substitutivity. Our experiments revealed that among the three types of composition, the models struggled most with systematicity, performing poorly even with relatively simple compositions. That difficulty was not resolved even after training the models with intermediate reasoning steps.

2022

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Twist Decoding: Diverse Generators Guide Each Other
Jungo Kasai | Keisuke Sakaguchi | Ronan Le Bras | Hao Peng | Ximing Lu | Dragomir Radev | Yejin Choi | Noah A. Smith
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Many language generation models are now available for a wide range of generation tasks, including machine translation and summarization. Combining such diverse models may lead to further progress, but ensembling generation models is challenging during inference: conventional ensembling methods (e.g., shallow fusion) require that the models share vocabulary/tokenization schemes. We introduce Twist decoding, a simple and general text generation algorithm that benefits from diverse models at inference time. Our method does not assume the vocabulary, tokenization or even generation order is shared. Our extensive evaluations on machine translation and scientific paper summarization demonstrate that Twist decoding substantially outperforms each model decoded in isolation over various scenarios, including cases where domain-specific and general-purpose models are both available. Twist decoding also consistently outperforms the popular reranking heuristic where output candidates from one model are rescored by another. We hope that our work will encourage researchers and practitioners to examine generation models collectively, not just independently, and to seek out models with complementary strengths to the currently available models.

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Transparent Human Evaluation for Image Captioning
Jungo Kasai | Keisuke Sakaguchi | Lavinia Dunagan | Jacob Morrison | Ronan Le Bras | Yejin Choi | Noah A. Smith
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We establish THumB, a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machine- and human-generated captions on the MSCOCO dataset. Each caption is evaluated along two main dimensions in a tradeoff (precision and recall) as well as other aspects that measure the text quality (fluency, conciseness, and inclusive language). Our evaluations demonstrate several critical problems of the current evaluation practice. Human-generated captions show substantially higher quality than machine-generated ones, especially in coverage of salient information (i.e., recall), while most automatic metrics say the opposite. Our rubric-based results reveal that CLIPScore, a recent metric that uses image features, better correlates with human judgments than conventional text-only metrics because it is more sensitive to recall. We hope that this work will promote a more transparent evaluation protocol for image captioning and its automatic metrics.

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Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
Jungo Kasai | Keisuke Sakaguchi | Ronan Le Bras | Lavinia Dunagan | Jacob Morrison | Alexander Fabbri | Yejin Choi | Noah A. Smith
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural language processing researchers have identified limitations of evaluation methodology for generation tasks, with new questions raised about the validity of automatic metrics and of crowdworker judgments. Meanwhile, efforts to improve generation models tend to depend on simple n-gram overlap metrics (e.g., BLEU, ROUGE). We argue that new advances on models and metrics should each more directly benefit and inform the other. We therefore propose a generalization of leaderboards, bidimensional leaderboards (Billboards), that simultaneously tracks progress in language generation models and metrics for their evaluation. Unlike conventional unidimensional leaderboards that sort submitted systems by predetermined metrics, a Billboard accepts both generators and evaluation metrics as competing entries. A Billboard automatically creates an ensemble metric that selects and linearly combines a few metrics based on a global analysis across generators. Further, metrics are ranked based on their correlation with human judgments. We release four Billboards for machine translation, summarization, and image captioning. We demonstrate that a linear ensemble of a few diverse metrics sometimes substantially outperforms existing metrics in isolation. Our mixed-effects model analysis shows that most automatic metrics, especially the reference-based ones, overrate machine over human generation, demonstrating the importance of updating metrics as generation models become stronger (and perhaps more similar to humans) in the future.

2021

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proScript: Partially Ordered Scripts Generation
Keisuke Sakaguchi | Chandra Bhagavatula | Ronan Le Bras | Niket Tandon | Peter Clark | Yejin Choi
Findings of the Association for Computational Linguistics: EMNLP 2021

Scripts – prototypical event sequences describing everyday activities – have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information. However, to date they have proved hard to author or extract from text. In this work, we demonstrate for the first time that pre-trained neural language models can be finetuned to generate high-quality scripts, at varying levels of granularity, for a wide range of everyday scenarios (e.g., bake a cake). To do this, we collect a large (6.4k) crowdsourced partially ordered scripts (named proScript), that is substantially larger than prior datasets, and develop models that generate scripts by combining language generation and graph structure prediction. We define two complementary tasks: (i) edge prediction: given a scenario and unordered events, organize the events into a valid (possibly partial-order) script, and (ii) script generation: given only a scenario, generate events and organize them into a (possibly partial-order) script. Our experiments show that our models perform well (e.g., F1=75.7 on task (i)), illustrating a new approach to overcoming previous barriers to script collection. We also show that there is still significant room for improvement toward human level performance. Together, our tasks, dataset, and models offer a new research direction for learning script knowledge.

2020

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A Dataset for Tracking Entities in Open Domain Procedural Text
Niket Tandon | Keisuke Sakaguchi | Bhavana Dalvi | Dheeraj Rajagopal | Peter Clark | Michal Guerquin | Kyle Richardson | Eduard Hovy
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary. For example, in a text describing fog removal using potatoes, a car window may transition between being foggy, sticky, opaque, and clear. Previous formulations of this task provide the text and entities involved, and ask how those entities change for just a small, pre-defined set of attributes (e.g., location), limiting their fidelity. Our solution is a new task formulation where given just a procedural text as input, the task is to generate a set of state change tuples (entity, attribute, before-state, after-state) for each step, where the entity, attribute, and state values must be predicted from an open vocabulary. Using crowdsourcing, we create OPENPI, a high-quality (91.5% coverage as judged by humans and completely vetted), and large-scale dataset comprising 29,928 state changes over 4,050 sentences from 810 procedural real-world paragraphs from WikiHow.com. A current state-of-the-art generation model on this task achieves 16.1% F1 based on BLEU metric, leaving enough room for novel model architectures.

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Uncertain Natural Language Inference
Tongfei Chen | Zhengping Jiang | Adam Poliak | Keisuke Sakaguchi | Benjamin Van Durme
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce Uncertain Natural Language Inference (UNLI), a refinement of Natural Language Inference (NLI) that shifts away from categorical labels, targeting instead the direct prediction of subjective probability assessments. We demonstrate the feasibility of collecting annotations for UNLI by relabeling a portion of the SNLI dataset under a probabilistic scale, where items even with the same categorical label differ in how likely people judge them to be true given a premise. We describe a direct scalar regression modeling approach, and find that existing categorically-labeled NLI data can be used in pre-training. Our best models correlate well with humans, demonstrating models are capable of more subtle inferences than the categorical bin assignment employed in current NLI tasks.

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The Universal Decompositional Semantics Dataset and Decomp Toolkit
Aaron Steven White | Elias Stengel-Eskin | Siddharth Vashishtha | Venkata Subrahmanyan Govindarajan | Dee Ann Reisinger | Tim Vieira | Keisuke Sakaguchi | Sheng Zhang | Francis Ferraro | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present the Universal Decompositional Semantics (UDS) dataset (v1.0), which is bundled with the Decomp toolkit (v0.1). UDS1.0 unifies five high-quality, decompositional semantics-aligned annotation sets within a single semantic graph specification—with graph structures defined by the predicative patterns produced by the PredPatt tool and real-valued node and edge attributes constructed using sophisticated normalization procedures. The Decomp toolkit provides a suite of Python 3 tools for querying UDS graphs using SPARQL. Both UDS1.0 and Decomp0.1 are publicly available at http://decomp.io.

2019

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WIQA: A dataset for “What if...” reasoning over procedural text
Niket Tandon | Bhavana Dalvi | Keisuke Sakaguchi | Peter Clark | Antoine Bosselut
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We introduce WIQA, the first large-scale dataset of “What if...” questions over procedural text. WIQA contains a collection of paragraphs, each annotated with multiple influence graphs describing how one change affects another, and a large (40k) collection of “What if...?” multiple-choice questions derived from these. For example, given a paragraph about beach erosion, would stormy weather hasten or decelerate erosion? WIQA contains three kinds of questions: perturbations to steps mentioned in the paragraph; external (out-of-paragraph) perturbations requiring commonsense knowledge; and irrelevant (no effect) perturbations. We find that state-of-the-art models achieve 73.8% accuracy, well below the human performance of 96.3%. We analyze the challenges, in particular tracking chains of influences, and present the dataset as an open challenge to the community.

2018

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Efficient Online Scalar Annotation with Bounded Support
Keisuke Sakaguchi | Benjamin Van Durme
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We describe a novel method for efficiently eliciting scalar annotations for dataset construction and system quality estimation by human judgments. We contrast direct assessment (annotators assign scores to items directly), online pairwise ranking aggregation (scores derive from annotator comparison of items), and a hybrid approach (EASL: Efficient Annotation of Scalar Labels) proposed here. Our proposal leads to increased correlation with ground truth, at far greater annotator efficiency, suggesting this strategy as an improved mechanism for dataset creation and manual system evaluation.

2017

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JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
Courtney Napoles | Keisuke Sakaguchi | Joel Tetreault
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for developing and evaluating grammatical error correction (GEC). Unlike other corpora, it represents a broad range of language proficiency levels and uses holistic fluency edits to not only correct grammatical errors but also make the original text more native sounding. We describe the types of corrections made and benchmark four leading GEC systems on this corpus, identifying specific areas in which they do well and how they can improve. JFLEG fulfills the need for a new gold standard to properly assess the current state of GEC.

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Error-repair Dependency Parsing for Ungrammatical Texts
Keisuke Sakaguchi | Matt Post | Benjamin Van Durme
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose a new dependency parsing scheme which jointly parses a sentence and repairs grammatical errors by extending the non-directional transition-based formalism of Goldberg and Elhadad (2010) with three additional actions: SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in derivation, we also introduce simple constraints that ensure the parser termination. We evaluate our model with respect to dependency accuracy and grammaticality improvements for ungrammatical sentences, demonstrating the robustness and applicability of our scheme.

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Grammatical Error Correction with Neural Reinforcement Learning
Keisuke Sakaguchi | Matt Post | Benjamin Van Durme
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We propose a neural encoder-decoder model with reinforcement learning (NRL) for grammatical error correction (GEC). Unlike conventional maximum likelihood estimation (MLE), the model directly optimizes towards an objective that considers a sentence-level, task-specific evaluation metric, avoiding the exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in human and automated evaluation metrics, achieving the state-of-the-art on a fluency-oriented GEC corpus.

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GEC into the future: Where are we going and how do we get there?
Keisuke Sakaguchi | Courtney Napoles | Joel Tetreault
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

The field of grammatical error correction (GEC) has made tremendous bounds in the last ten years, but new questions and obstacles are revealing themselves. In this position paper, we discuss the issues that need to be addressed and provide recommendations for the field to continue to make progress, and propose a new shared task. We invite suggestions and critiques from the audience to make the new shared task a community-driven venture.

2016

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Universal Decompositional Semantics on Universal Dependencies
Aaron Steven White | Drew Reisinger | Keisuke Sakaguchi | Tim Vieira | Sheng Zhang | Rachel Rudinger | Kyle Rawlins | Benjamin Van Durme
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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There’s No Comparison: Reference-less Evaluation Metrics in Grammatical Error Correction
Courtney Napoles | Keisuke Sakaguchi | Joel Tetreault
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality
Keisuke Sakaguchi | Courtney Napoles | Matt Post | Joel Tetreault
Transactions of the Association for Computational Linguistics, Volume 4

The field of grammatical error correction (GEC) has grown substantially in recent years, with research directed at both evaluation metrics and improved system performance against those metrics. One unvisited assumption, however, is the reliance of GEC evaluation on error-coded corpora, which contain specific labeled corrections. We examine current practices and show that GEC’s reliance on such corpora unnaturally constrains annotation and automatic evaluation, resulting in (a) sentences that do not sound acceptable to native speakers and (b) system rankings that do not correlate with human judgments. In light of this, we propose an alternate approach that jettisons costly error coding in favor of unannotated, whole-sentence rewrites. We compare the performance of existing metrics over different gold-standard annotations, and show that automatic evaluation with our new annotation scheme has very strong correlation with expert rankings (ρ = 0.82). As a result, we advocate for a fundamental and necessary shift in the goal of GEC, from correcting small, labeled error types, to producing text that has native fluency.

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Phrase Structure Annotation and Parsing for Learner English
Ryo Nagata | Keisuke Sakaguchi
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Ground Truth for Grammatical Error Correction Metrics
Courtney Napoles | Keisuke Sakaguchi | Matt Post | Joel Tetreault
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Effective Feature Integration for Automated Short Answer Scoring
Keisuke Sakaguchi | Michael Heilman | Nitin Madnani
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Efficient Elicitation of Annotations for Human Evaluation of Machine Translation
Keisuke Sakaguchi | Matt Post | Benjamin Van Durme
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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Discriminative Approach to Fill-in-the-Blank Quiz Generation for Language Learners
Keisuke Sakaguchi | Yuki Arase | Mamoru Komachi
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Construction of English MWE Dictionary and its Application to POS Tagging
Yutaro Shigeto | Ai Azuma | Sorami Hisamoto | Shuhei Kondo | Tomoya Kose | Keisuke Sakaguchi | Akifumi Yoshimoto | Frances Yung | Yuji Matsumoto
Proceedings of the 9th Workshop on Multiword Expressions

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NAIST at the NLI 2013 Shared Task
Tomoya Mizumoto | Yuta Hayashibe | Keisuke Sakaguchi | Mamoru Komachi | Yuji Matsumoto
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

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NAIST at 2013 CoNLL Grammatical Error Correction Shared Task
Ippei Yoshimoto | Tomoya Kose | Kensuke Mitsuzawa | Keisuke Sakaguchi | Tomoya Mizumoto | Yuta Hayashibe | Mamoru Komachi | Yuji Matsumoto
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

2012

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NAIST at the HOO 2012 Shared Task
Keisuke Sakaguchi | Yuta Hayashibe | Shuhei Kondo | Lis Kanashiro | Tomoya Mizumoto | Mamoru Komachi | Yuji Matsumoto
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Joint English Spelling Error Correction and POS Tagging for Language Learners Writing
Keisuke Sakaguchi | Tomoya Mizumoto | Mamoru Komachi | Yuji Matsumoto
Proceedings of COLING 2012