Papers by Manabu Okumura
One-class Text Classification with Multi-modal Deep Support Vector Data Description (2021.eacl-main)
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| Challenge: | Using multi-modal deep SVDD, we can build a much better description for target one-class data. |
| Approach: | They propose to extend uni-modal SVDD to multiple modal mSVDD and introduce a mechanism for incorporating negative supervision in the absence of real negative data. |
| Outcome: | The proposed model outperforms uni-modal SVDD and can get further improvements when negative supervision is incorporated. |
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing methods for text infilling focus on the infill length of blanks and attribute relevance, but attribute-aware content can be more useful. |
| Approach: | They propose an attribute-aware text infilling method via a Pre-trained language model which contains a text in filling component and a plug-and-play discriminator. |
| Outcome: | The proposed method improves attribute relevance without decreasing text fluency on three open-source datasets. |
Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token (2026.acl-long)
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| Challenge: | Existing methods modify attention mechanism to be bidirectional, undermining LLMs’ ability to extract semantic information acquired during pre-training. |
| Approach: | They propose a general-purpose embedding model that pre-encodes input text into a single Contextual token and then prepends it to the LLM's input sequence. |
| Outcome: | The proposed model improves performance of decoder-only large language models without altering their architectures or introducing significant computational overhead. |
Length Representations in Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown remarkable capabilities across various tasks, that are learned from massive amounts of text-based data. |
| Approach: | They propose to scale hidden units within the model to control output sequence length without losing the informativeness of the generated text. |
| Outcome: | The output sequence length is controlled by multiple head attention mechanisms, which can be adjusted in a disentangled manner. |
Pointing to Subwords for Generating Function Names in Source Code (2020.coling-main)
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| Challenge: | Existing methods for generating function names from source code face difficulties in generating low-frequency or out-of-vocabulary subwords. |
| Approach: | They propose two strategies for copying low-frequency or out-of-vocabulary subwords in inputs. |
| Outcome: | The proposed method improves on the Java-small and Java-large datasets and improves the existing method on the GitHub platform. |
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing (2022.findings-emnlp)
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| Challenge: | Existing discourse parsing methods need a strong baseline for reporting reliable experimental results. |
| Approach: | They integrate existing parsing strategies with transformer-based pre-trained language models to provide a strong baseline for reporting reliable experimental results. |
| Outcome: | The proposed model outperforms the current best model using DeBERTa. |
Split or Merge: Which is Better for Unsupervised RST Parsing? (D19-1)
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| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |
Aspect-based Analysis of Advertising Appeals for Search Engine Advertising (2022.naacl-industry)
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| Challenge: | ad creators must consider various aspects of advertising appeals such as price, product features, and quality in their ac work. |
| Approach: | They propose to use a dataset of ad texts to explore the effective aspects of advertising appeals (A3) for different industries to assist a search engine ap creators. |
| Outcome: | The proposed model can detect aspects of ad texts and help them estimate their performance. |
Generative Replay Inspired by Hippocampal Memory Indexing for Continual Language Learning (2023.eacl-main)
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| Challenge: | Continual learning (CL) is a fundamental requirement for human-like general intelligence (Parisi et al., 2019). |
| Approach: | They propose to control sample generation using compressed features of previous training samples by using hippocampal memory indexing to enhance the generative replay. |
| Outcome: | The proposed method outperforms current generative replay methods and generates training samples from previous tasks. |
Abstractive Document Summarization with Summary-length Prediction (2023.findings-eacl)
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| Challenge: | Existing abstractive summarization models do not consider summarizing-specific information such as the target summary length. |
| Approach: | They propose a method for enabling a model to understand summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in fine-tuning. |
| Outcome: | The proposed method improves ROUGE scores on the WikiHow, NYT, and CNN/DM datasets. |
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation (2025.acl-long)
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| Challenge: | Maximum a posteriori decoding aims to maximize the estimated posterior probability, but high estimated probability does not always lead to high translation quality. |
| Approach: | They propose a method that seeks hypotheses with the highest expected utility by using quasi-sources as “support hypothese . they propose sMBR decoding which utilizes a reference-free quality estimation metric as the utility function. |
| Outcome: | The proposed approach outperforms QE reranking and the standard MBR decoding. |
Context-aware Neural Machine Translation with Coreference Information (D19-65)
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| Challenge: | Existing models for translating a sentence in a text do not consider coreference relations provided within the text. |
| Approach: | They propose a graph-based encoder which can consider coreference relations provided within the text explicitly. |
| Outcome: | The proposed model improves on the previous approach by 0.9 points on the BLEU score . the graph-based encoder can handle a longer text well, compared with the previous model . |
Neural Machine Translation Incorporating Named Entity (C18-1)
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| Challenge: | Conventional NMT models have difficulty translating words with multiple meanings because of the high ambiguity. |
| Approach: | They propose a neural machine translation model that incorporates named entity (NE) tags of source-language sentences to reduce the difficulty in translating multiple meanings. |
| Outcome: | The proposed model achieves 3.11 point improvement in bilingual evaluation understudy (BLEU) on English-to-Japanese translation task with the ASPEC, and English- to-Bulgarian and English to-Romanian translation tasks with the Europarl corpus. |
Neural text normalization leveraging similarities of strings and sounds (2020.coling-main)
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| Challenge: | Existing methods that ignore the similarities of word strings and sounds do not account for these features. |
| Approach: | They propose a neural model that considers the similarities of both word strings and sounds, and a model that takes only the similarity of word strings or of sounds as a baseline. |
| Outcome: | The proposed models outperformed a baseline model and achieved state-of-the-art results on WNUT-2015. |
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimoda Emotion Recognition (2023.emnlp-main)
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| Challenge: | Existing graph-based methods fail to depict global contextual features and local diverse unimodal features in a dialogue. |
| Approach: | They propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition using a multimodal fusion mechanism and a graph contrastative learning framework. |
| Outcome: | The proposed method improves multimodal emotion recognition on unbalanced and small-scale emotional datasets. |
AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset (2025.findings-acl)
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| Challenge: | Identifying factors that make ad text attractive is essential for advertising success . identifying the linguistic factors presents a significant challenge because of the intricate interplay between the semantic content and its linguistic expression. |
| Approach: | They propose to use a dataset for ad text paraphrasing that contains human preference data to enable analysis of linguistic factors. |
| Outcome: | The proposed dataset is 20 times larger than v1.0 and contains 16,460 pairs of ad text paraphrase pairs . it shows that human preference and ade- t attractiveness are related . |
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer (2021.emnlp-main)
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| Challenge: | Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. |
| Approach: | They propose a nested tree-based extractive summarization model on RoBERTa that uses syntactic and discourse trees to represent sentences in a given document. |
| Outcome: | The proposed model outperforms baseline models on the CNN/DailyMail dataset and achieves significantly better scores than the baseline models in terms of coherence and comparable scores to the state-of-the-art models. |
Measuring Watermarking under Jailbreaking: ASR Inflation and Goal-Compliance Mismatch (2026.findings-acl)
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| Challenge: | Recent work studies watermarking under benign prompts, but its behavior under jailbreaking prompts remains underexplored. |
| Approach: | They evaluate six methods on four LLMs using two jailbreak benchmarks and three settings: Static, AutoDAN, and DSN. |
| Outcome: | The proposed methods inflate judge-based attack success rate under jailbreaking, but not harmful-goal compliance. |
Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization (2024.findings-emnlp)
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| Challenge: | Abstractive text summarization (ATS) requires laborious data annotation and time-consuming model training. |
| Approach: | They propose a novel active learning framework that asks large language models to rate difficulty of instances and then uses certainty gain maximization to select instances with a distribution that aligns well with the overall distribution. |
| Outcome: | The proposed framework improves stability, effectiveness, and efficiency of abstractive text summarization backbones. |
An Empirical Study of Generating Texts for Search Engine Advertising (2021.naacl-industry)
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| Challenge: | Existing studies on neural language generation have not evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. |
| Approach: | They propose to integrate a reinforcement learning framework into an end-to-end sequence-tosequence (Seq2S) model and demonstrate how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads. |
| Outcome: | The proposed method improves the ads’ impact, deploys the models to a product, and evaluates the generated ads. |
Focused Prefix Tuning for Controllable Text Generation (2023.acl-short)
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| Challenge: | Existing unannotated attributes could degrade models' performance . focus on the desired attribute can be achieved with focused prefix tuning . |
| Approach: | They propose focused prefix tuning to enable the control to focus on the desired attribute . they propose to reduce the number of unannotated attributes in a controllable text generation dataset . |
| Outcome: | The proposed approach achieves better control accuracy and text fluency than baseline models in single-attribute tasks. |
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have been widely explored for embedding generation. |
| Approach: | They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden. |
| Outcome: | The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark. |
JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation (2022.coling-1)
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| Challenge: | Existing methods rarely consider cross-modal alignment between textual and visual features and ignore disease tags as auxiliary for report generation. |
| Approach: | They propose a "Jointly learning framework for automated disease Prediction and radiology report Generation" the framework integrates cross-modal alignment between textual and visual features and disease tags to improve the quality of reports. |
| Outcome: | The proposed framework improves the quality of radiology reports by combining the main task and auxiliary tasks. |
TACR: A Table Alignment-based Cell Selection Method for HybridQA (2023.findings-acl)
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| Challenge: | Hybrid Question-Answering datasets lack a robust reasoning model for text-based QA. |
| Approach: | They propose a table-question-alignment-based cell-selection and reasoning model for hybrid text and table QA. |
| Outcome: | The proposed model outperforms baselines on HybridQA and WikiTableQuestions datasets on cell selection and argumentation. |
Hierarchical Trivia Fact Extraction from Wikipedia Articles (2020.coling-main)
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| Challenge: | Existing methods for extracting trivia facts for Wikipedia categories are not efficient . a trivia fact is an interesting fact that is unusual, unexpected, or unique . |
| Approach: | They propose an unsupervised algorithm that automatically mines trivia facts for a given entity . they propose to target at a single Wikipedia article and leverage its hierarchical structure . |
| Outcome: | The proposed algorithm outperforms existing methods and is 100 times faster than existing methods. |
Improving Neural RST Parsing Model with Silver Agreement Subtrees (2021.naacl-main)
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| Challenge: | Existing methods for Rhetorical Structure Theory (RST) parsing use supervised learning, but the RST-DT is small due to the costly annotation of RST trees. |
| Approach: | They propose to use silver data to improve RST parsing models by using annotated silver data. |
| Outcome: | The proposed method achieves the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2 . it also achieves a remarkable gain in relation score against the previous state-of-the-art parser. |
Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
| Approach: | They propose a framework for numerical table-to-text generation based on numerical reasoning . they use a pre-trained model and a copy mechanism to fine-tune the models to produce fluent text . |
| Outcome: | The proposed framework lacks fidelity to the table contents and is based on a pre-trained model and a copy mechanism. |
A New Surprise Measure for Extracting Interesting Relationships between Persons (2021.eacl-demos)
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| Challenge: | Interesting facts are useful information for a variety of important tasks. |
| Approach: | They propose a method that extracts all personal relationships from dependency trees and calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. |
| Outcome: | The proposed method extracts all personal relationships from dependency trees for the texts and calculates surprise scores for distributed representations of the extracted relationships in an unsupervised manner. |
Hierarchical Label Generation for Text Classification (2023.findings-eacl)
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| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
| Approach: | They propose a method that captures the label hierarchy for real-world classification applications by using a taxonomic hierarchy. |
| Outcome: | The proposed method can generate unseen labels in subword level. |
Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers (2021.eacl-main)
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| Challenge: | Numerical tables are used to present experimental results in scientific papers. |
| Approach: | They propose a task to extract metric-types from multi-level header numerical tables . they propose two joint-learning neural classification and generation schemes . |
| Outcome: | The proposed models handle in-header and out-of-headers metric-type identification problems. |
Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
| Approach: | They propose a method for improving BERT's performance by using a label embedding technique while keeping almost the same computational cost. |
| Outcome: | The proposed method improves BERT's performance on six text classification benchmark datasets while keeping almost the same computational cost. |
Generating Repetitions with Appropriate Repeated Words (2022.naacl-main)
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| Challenge: | Existing studies have focused on general response generation with neural network-based approaches, but none have addressed specific types of repetitions. |
| Approach: | They propose a weighted label smoothing method for explicitly learning which words to repeat during fine-tuning and a repetition scoring method that can output more appropriate repetitions during decoding. |
| Outcome: | The proposed method outperforms baselines in automatic and human evaluations on a pre-trained language model for generating repetitions. |
InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models (2024.findings-acl)
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| Challenge: | Existing sentences do not consider the length constraints in extractive summarization because of their limited model abilities. |
| Approach: | They propose an approach that incorporates length constraints without model modifications into sentences . they use traditional sentence compression datasets to transform them into instruction format . |
| Outcome: | The proposed method can consider the length constraint through instructions without model modifications. |
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation (2024.findings-naacl)
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| Challenge: | Existing methods to extract word embeddings from training datasets are not efficient for training other models. |
| Approach: | They propose a method to distill a training dataset into a textual model by combining a small number of informative synthetic samples. |
| Outcome: | The proposed method outperforms existing methods on training datasets and language models. |
Emotion Recognition in Multi-Speaker Conversations through Speaker Identification, Knowledge Distillation, and Hierarchical Fusion (2026.findings-eacl)
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| Challenge: | Emotion recognition in multi-speaker conversations faces significant challenges due to speaker ambiguity and severe class imbalance. |
| Approach: | They propose a speaker identification module that leverages audio-visual synchronization to accurately identify the active speaker and hierarchical attention fusion with composite loss functions to handle class imbalance. |
| Outcome: | The proposed framework achieves 67.75% and 72.44% weighted F1 scores on MELD and IEMOCAP datasets, with notable improvements on minority emotion classes. |
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization (2022.naacl-main)
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| Challenge: | Existing studies on timeline summarization ignore the information interaction between sentences and dates, and combine them as two separate tasks. |
| Approach: | They propose a joint learning-based heterogeneous graph attention network for timeline summarization (HeterTls) they combine date selection and event detection into a unified framework to improve extraction accuracy . |
| Outcome: | The proposed model outperforms state-of-the-art models on four datasets . it significantly outperformed the baseline models on ROUGE scores and date selection metrics . |
Progressive Visual Refinement for Multi-modal Summarization (2026.eacl-short)
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| Challenge: | Multi-modal summarization (MMS) is a critical research area driven by the proliferation of multimedia content. |
| Approach: | They propose a patch-refined visual information network to exploit multimodal information . they propose combining visual information with textual information to generate concise summaries . |
| Outcome: | Extensive experiments on two public MMS datasets show the superiority of the proposed model. |
Considering Length Diversity in Retrieval-Augmented Summarization (2025.findings-naacl)
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| Challenge: | Existing methods that require exhaustive exemplar-exemplar relevance comparisons do not consider summary lengths. |
| Approach: | They propose a Diverse Length-aware Maximal Marginal Relevance algorithm to better control summary lengths. |
| Outcome: | The proposed algorithm reduces the computational cost and memory consumption while maintaining the same level of informativeness. |
Bidirectional Transformer Reranker for Grammatical Error Correction (2023.findings-acl)
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| Challenge: | Pre-trained seq2seq models suffer from a prediction bias due to their unidirectional decoding. |
| Approach: | They propose a bidirectional Transformer reranker that re-estimates the probability of each candidate sentence generated by pre-trained seq2seq models. |
| Outcome: | The proposed model improves on the original model and gives a 59.52 GLEU score on the JFLEG corpus. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation (2024.findings-emnlp)
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| Challenge: | Multi-modal machine translation methods are underperforming compared to pre-trained models due to lack of triplet training data. |
| Approach: | They propose a multi-modal machine translation method that integrates images and visual modality to enhance language understanding. |
| Outcome: | The proposed method can enrich the original samples and expand the dataset without requiring external images and text. |
Dataset Distillation with Attention Labels for Fine-tuning BERT (2023.acl-short)
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| Challenge: | Specifically, we propose to introduce attention labels, which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities. |
| Approach: | They propose to introduce attention labels which can efficiently distill the knowledge from the original dataset and transfer it to the transformer models via attention probabilities. |
| Outcome: | The proposed methods perform impressively in four different NLP tasks and achieve 93.2% accuracy in AGNews, which is 98.5% of the original dataset even with only one sample per class and only one gradient step. |
Dataset Creation for Ranking Constructive News Comments (P19-1)
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| Challenge: | Existing studies on comment ranking use user feedback as a quality measure . however, this type of measurement has two drawbacks: (a) user feedback does not always satisfy the service provider's needs, such as to create a fair place; and (b) user input will be biased by where comments appear in a comment thread. |
| Approach: | They propose to evaluate quality of comments on the basis of constructiveness separately from user feedback. |
| Outcome: | The proposed model improves on 100K+ Japanese comments with constructiveness scores . it shows that C-scores are not always related to positive feedback . |
Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification (2024.eacl-short)
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| Challenge: | Existing methods for fine-tuning pre-trained models require massive computational resources and time. |
| Approach: | They propose a novel approach for fine-tuning a pre-trained model using backpropagation and an iterative extreme learning machine for training a classifier. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in training-time measurement and performance with comparable model performance. |
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing (2021.emnlp-main)
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| Challenge: | Existing methods to consider textual coherence are limited in labeled data. |
| Approach: | They propose a language model-based generative classifier that uses labels as input and embeds labels into their representations. |
| Outcome: | The proposed classifier achieves state-of-the-art in discourse segmentation and relation F1 scores with gold boundaries and automatically segmented boundaries. |
ConRAS: Contrastive In-context Learning Framework for Retrieval-Augmented Summarization (2026.findings-eacl)
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| Challenge: | Despite progress in natural language processing, the potential of contrastive learning remains unexplored. |
| Approach: | They propose a framework that injects contrastive objectives into in-context learning-based retrieval-augmented summarization. |
| Outcome: | The proposed framework outperforms state-of-the-art retrieval-augmented methods on three summarization benchmarks showing that it can distinguish between positive and negative samples without parameter updates. |
Video Discourse Parsing and Its Application to Multimodal Summarization: A Dataset and Baseline Approaches (2024.findings-emnlp)
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| Challenge: | Fig. 1 shows the video's story structure and event relationships in discourse parsing. |
| Approach: | They propose to construct an RST tree for a video to represent its storyline and illustrate the event relationships between events. |
| Outcome: | The proposed model outperforms two existing approaches to video RST parsing: the ‘parsing after captioning’ framework and parser using visual features. |
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs (2020.coling-main)
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| Challenge: | Community-based question answering platforms take time to get useful information from among many answers. |
| Approach: | They propose a method to select a diverse and non-redundant answer set rather than ranking the answers. |
| Outcome: | The proposed method outperforms baseline methods on a Japanese CQA site . it calculates the answer importance and similarity between answers by using BERT . |
AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts (2025.findings-naacl)
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| Challenge: | Effective linguistic choices that attract potential customers play crucial roles in advertising success. |
| Approach: | They propose to use a paraphrase dataset to explore linguistic features of ad texts that influence human preferences to maximize the potential success of advertisements. |
| Outcome: | The proposed model improves the attractiveness of ad texts by focusing on human preferences. |
Debiasing Large Language Models with Structured Knowledge (2024.findings-acl)
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| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
| Approach: | They propose a method that utilizes structured knowledge to mitigate bias in LLMs . their method obviates the need for training from scratch, thus offering enhanced scalability . |
| Outcome: | The proposed method outperforms state-of-the-art (SOTA) baselines in the debiasing ability. |
Can we obtain significant success in RST discourse parsing by using Large Language Models? (2024.eacl-long)
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| Challenge: | Experimental results show that LLMs with tens of billion parameters can perform discourse parsing tasks. |
| Approach: | They employ Llama 2 and fine-tune it with QLoRA to achieve similar results . they show that LLMs with tens of billion parameters can perform a wide range of NLP tasks . |
| Outcome: | The proposed model performs better than existing models on three benchmark datasets. |
Live Football Commentary System Providing Background Information (2025.acl-demo)
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Yuichiro Mori, Chikara Tanaka, Aru Maekawa, Satoshi Kosugi, Tatsuya Ishigaki, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | Existing studies on sports commentary generation focus on describing major events in the video, but real-world commentary often includes background information. |
| Approach: | They developed an audio commentary system that generates utterances with background information and play-by-play commentary for football matches. |
| Outcome: | The proposed system generates utterances with background information and play-by-play commentary for football matches. |
Efficient Entity Embedding Construction from Type Knowledge for BERT (2022.findings-aacl)
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| Challenge: | Existing work has shown advantages of incorporating knowledge graphs (KGs) into BERT for various NLP tasks. |
| Approach: | They propose to integrate knowledge graphs into BERT to train entity embeddings to include rich information of factual knowledge. |
| Outcome: | The proposed models perform very well when combined with context. |
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding (2026.findings-acl)
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Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito, Jingun Kwon, Manabu Okumura, Taro Watanabe
| Challenge: | Existing methods to improve summarization quality are limited to using source as guidance . reranking can be effective, but there are limitations, such as relying on reference-free metrics and rely on a single metric. |
| Approach: | They propose a model that reranks model-generated summaries by considering consistency to the source document and consensus among the other candidates. |
| Outcome: | The proposed system is competitive with existing methods, with human evaluations further confirming that it is superior. |
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction (2022.coling-1)
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Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
| Challenge: | Existing methods to extract opinion words from sentences are limited due to the expensive annotation process. |
| Approach: | They propose to exploit massive unlabeled data to reduce distribution shift risk . they propose to use two filters specifically for TOWE to filter noisy data . results indicate superiority of MGCR over current state-of-the-art methods . |
| Outcome: | The proposed method reduces the risk of distribution shifts by increasing the exposure of the model to varying distribution shift. |
Generating Weather Comments from Meteorological Simulations (2021.eacl-main)
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Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | generating weather-forecast comments from meteorological simulations is labor intensive and requires a solid knowledge of meteorology. |
| Approach: | They propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. |
| Outcome: | The proposed model performs best against baselines in terms of informativeness . it is available online and the results are available to the general public . |
A Simple and Effective Usage of Word Clusters for CBOW Model (2020.aacl-main)
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| Challenge: | Existing word clustering algorithms can be used to obtain word embeddings without additional language resources. |
| Approach: | They propose to replace infrequent input and output words with clusters to produce word embeddings. |
| Outcome: | The proposed method produces embeddings of frequent words and small amount of cluster embeddables, which can be fine-tuned on downstream tasks. |