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Found 61 papers, 18 papers with code
A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization
Lexical normalization, in addition to word segmentation and part-of-speech tagging, is a fundamental task for Japanese user-generated text processing.
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation
Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT.
Improving Discriminative Learning for Zero-Shot Relation Extraction
Zero-shot relation extraction (ZSRE) aims to predict target relations that cannot be observed during training.
Ranked #3 on
Zero-shot Relation Classification
on FewRel
Transliteration for Low-Resource Code-Switching Texts: Building an Automatic Cyrillic-to-Latin Converter for Tatar
Given this situation, we proposed a transliteration method based on subword-level language identification.
Universal Dependencies Treebank for Tatar: Incorporating Intra-Word Code-Switching Information
Experimenting on NMCTT and the Turkish-German CS treebank (SAGT), we demonstrate that the proposed annotation scheme introduced in NMCTT can improve the performance of the subword-level language identification.
Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space
A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world.
Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair
In Simultaneous Machine Translation (SiMT) systems, training with a simultaneous interpretation (SI) corpus is an effective method for achieving high-quality yet low-latency systems.
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models
Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites, and it is a truly demanding task as paper and electronic forms of documents are so common in our society.
Cross-lingual Contextualized Phrase Retrieval
In our work, we propose a new task formulation of dense retrieval, cross-lingual contextualized phrase retrieval, which aims to augment cross-lingual applications by addressing polysemy using context information.
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models
Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as patents, papers, and theses, without having domain-specific knowledge.
Artwork Explanation in Large-scale Vision Language Models
To address this issue, we propose a new task: the artwork explanation generation task, along with its evaluation dataset and metric for quantitatively assessing the understanding and utilization of knowledge about artworks.
Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?
Experimental results on the Stack-Overflow dataset and the TSCC dataset, including multi-turn conversation show that LLMs can implicitly handle text difficulty between user input and its generated response.
Evaluating Image Review Ability of Vision Language Models
Large-scale vision language models (LVLMs) are language models that are capable of processing images and text inputs by a single model.
Centroid-Based Efficient Minimum Bayes Risk Decoding
Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation performance by using COMET, a neural metric that has a high correlation with human evaluation.
Generating Diverse Translation with Perturbed kNN-MT
Generating multiple translation candidates would enable users to choose the one that satisfies their needs.
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?
Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG.
knn-seq: Efficient, Extensible kNN-MT Framework
We publish our knn-seq as an MIT-licensed open-source project and the code is available on https://github. com/naist-nlp/knn-seq .
Model-based Subsampling for Knowledge Graph Completion
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets.
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.
Ranked #89 on
Visual Question Answering
on MM-Vet
Japanese Lexical Complexity for Non-Native Readers: A New Dataset
Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale.
Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction
This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task.
Ranked #1 on
Zero-shot Relation Classification
on FewRel
Second Language Acquisition of Neural Language Models
With the success of neural language models (LMs), their language acquisition has gained much attention.
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models
This task consists of two parts: the first is to generate a table containing knowledge about an entity and its related image, and the second is to generate an image from an entity with a caption and a table containing related knowledge of the entity.
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
Geoparsing is a fundamental technique for analyzing geo-entity information in text.
Arukikata Travelogue Dataset
We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research.
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning
Discriminativeness is a desirable feature of image captions: captions should describe the characteristic details of input images.
$N$-gram Is Back: Residual Learning of Neural Text Generation with $n$-gram Language Model
The combination of $n$-gram and neural LMs not only allows the neural part to focus on the deeper understanding of language but also provides a flexible way to customize an LM by switching the underlying $n$-gram model without changing the neural model.
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation
Multilingual neural machine translation can translate unseen language pairs during training, i. e. zero-shot translation.
Visualizing the Relationship Between Encoded Linguistic Information and Task Performance
To this end, we study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality.
Improved Decomposition Strategy for Joint Entity and Relation Extraction
A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity.
Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding
For knowledge graph completion, two major types of prediction models exist: one based on graph embeddings, and the other based on relation path rule induction.
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing
Abstract Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure.
Neural Machine Translation with Synchronous Latent Phrase Structure
It is reported that grammatical information is useful for machine translation (MT) task.
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path
This paper presents a novel method for nested named entity recognition.
Ranked #12 on
Nested Named Entity Recognition
on GENIA
Zero Pronouns Identification based on Span prediction
In the experiments, our model surpassed the sequence labeling baseline.
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning
Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images.
User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization
Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT).
Demystifying Learning of Unsupervised Neural Machine Translation
Unsupervised Neural Machine Translation or UNMT has received great attention in recent years.
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation
We propose a simple method for nominal coordination boundary identification.
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection
Measuring domain relevance of data and identifying or selecting well-fit domain data for machine translation (MT) is a well-studied topic, but denoising is not yet.
Phrase-based Machine Translation using Multiple Preordering Candidates
In this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure.
