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Found 96 papers, 38 papers with code
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.
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.
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
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.
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.
Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?
One of the goals of automatic evaluation metrics in grammatical error correction (GEC) is to rank GEC systems such that it matches human preferences.
Tonguescape: Exploring Language Models Understanding of Vowel Articulation
In this study, we created video and image datasets from the existing real-time MRI dataset and investigated whether LMs can understand vowel articulation based on tongue positions using vision-based information.
Measuring the Robustness of Reference-Free Dialogue Evaluation Systems
Advancements in dialogue systems powered by large language models (LLMs) have outpaced the development of reliable evaluation metrics, particularly for diverse and creative responses.
Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity
Various measures of dispersion have been proposed to paint a fuller picture of a word's distribution in a corpus, but only little has been done to validate them externally.
Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation
We further scale up and collect 9. 3 billion sentence pairs across 24 languages from public datasets to pre-train two models, namely MITRE (multilingual translation with registers).
Understanding the Impact of Confidence in Retrieval Augmented Generation: A Case Study in the Medical Domain
Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries.
CoAM: Corpus of All-Type Multiword Expressions
MWEs in CoAM are tagged with MWE types, such as Noun and Verb, to enable fine-grained error analysis.
Improving Explainability of Sentence-level Metrics via Edit-level Attribution for Grammatical Error Correction
Various evaluation metrics have been proposed for Grammatical Error Correction (GEC), but many, particularly reference-free metrics, lack explainability.
Improving Language Transfer Capability of Decoder-only Architecture in Multilingual Neural Machine Translation
We propose dividing the decoding process into two stages so that target tokens are explicitly excluded in the first stage to implicitly boost the transfer capability across languages.
Difficult for Whom? A Study of Japanese Lexical Complexity
The tasks of lexical complexity prediction (LCP) and complex word identification (CWI) commonly presuppose that difficult to understand words are shared by the target population.
Graph-Structured Trajectory Extraction from Travelogues
Previous studies on sequence-based extraction of human movement trajectories have an issue of inadequate trajectory representation.
Theoretical Aspects of Bias and Diversity in Minimum Bayes Risk Decoding
We also introduce a new MBR approach, Metric-augmented MBR (MAMBR), which increases diversity by adjusting the behavior of utility functions without altering the pseudo-references.
BQA: Body Language Question Answering Dataset for Video Large Language Models
To address this, we propose a dataset, BQA, a body language question answering dataset, to validate whether the model can correctly interpret emotions from short clips of body language comprising 26 emotion labels of videos of body language.
WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines
This benchmark includes a visual question answering (VQA) dataset with text-image pairs across 30 languages and dialects, spanning 9 language families and featuring over 1 million data points, making it the largest multicultural VQA benchmark to date.
Can Language Models Induce Grammatical Knowledge from Indirect Evidence?
What kinds of and how much data is necessary for language models to induce grammatical knowledge to judge sentence acceptability?
ALR$^2$: A Retrieve-then-Reason Framework for Long-context Question Answering
The context window of large language models (LLMs) has been extended significantly in recent years.
Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?
Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words even in the era of large language models (LLMs).
Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation
Furthermore, we propose architecture learning techniques and introduce a gradual pruning schedule during fine-tuning to exhaustively explore the optimal setting and the minimal intrinsic subspaces for each language, resulting in a lightweight yet effective fine-tuning procedure.
Towards Cross-Lingual Explanation of Artwork in Large-scale Vision Language Models
As the performance of Large-scale Vision Language Models (LVLMs) improves, they are increasingly capable of responding in multiple languages, and there is an expectation that the demand for explanations generated by LVLMs will grow.
Toward the Evaluation of Large Language Models Considering Score Variance across Instruction Templates
In this study, we provide English and Japanese cross-lingual datasets for evaluating the NLU performance of LLMs, which include multiple instruction templates for fair evaluation of each task, along with regular expressions to constrain the output format.
How to Make the Most of LLMs' Grammatical Knowledge for Acceptability Judgments
The grammatical knowledge of language models (LMs) is often measured using a benchmark of linguistic minimal pairs, where LMs are presented with a pair of acceptable and unacceptable sentences and required to judge which is acceptable.
AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising
With the increase in the more fluent ad texts automatically created by natural language generation technology, it is in the high demand to verify the quality of these creatives in a real-world setting.
mbrs: A Library for Minimum Bayes Risk Decoding
We published our mbrs as an MIT-licensed open-source project, and the code is available on GitHub.
Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
This paper provides theoretical interpretations of the smoothing methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive Negative Sampling (TANS), that can cover the characteristics of the conventional smoothing methods.
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?
This work investigates the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks.
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks
Trustworthy prediction in Deep Neural Networks (DNNs), including Pre-trained Language Models (PLMs) is important for safety-critical applications in the real world.
Change My Frame: Reframing in the Wild in r/ChangeMyView
Recent work in reframing, within the scope of text style transfer, has so far made use of out-of-context, task-prompted utterances in order to produce neutralizing or optimistic reframes.
Towards Temporal Change Explanations from Bi-Temporal Satellite Images
Explaining temporal changes between satellite images taken at different times is important for urban planning and environmental monitoring.
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications.
Attention Score is not All You Need for Token Importance Indicator in KV Cache Reduction: Value Also Matters
Scaling the context size of large language models (LLMs) enables them to perform various new tasks, e. g., book summarization.
Languages Transferred Within the Encoder: On Representation Transfer in Zero-Shot Multilingual Translation
Understanding representation transfer in multilingual neural machine translation can reveal the representational issue causing the zero-shot translation deficiency.
mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans
Constructed dataset is a benchmark for cross-lingual language-transfer capabilities of multilingual LMs, and experimental results showed high language-transfer capabilities for questions that LMs could easily solve, but lower transfer capabilities for questions requiring deep knowledge or commonsense.
Context-Aware Machine Translation with Source Coreference Explanation
Despite significant improvements in enhancing the quality of translation, context-aware machine translation (MT) models underperform in many cases.
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.
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
Our contribution is four-fold: 1) we introduced nine vision-and-language (VL) tasks (including object recognition, image-text matching, and more) and constructed multilingual visual-text datasets in four languages: English, Japanese, Swahili, and Urdu through utilizing templates containing \textit{questions} and prompting GPT4-V to generate the \textit{answers} and the \textit{rationales}, 2) introduced a new VL task named \textit{unrelatedness}, 3) introduced rationales to enable human understanding of the VLM reasoning process, and 4) employed human evaluation to measure the suitability of proposed datasets for VL tasks.
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.
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili
Tokenization is one such technique because it allows for the words to be split based on characters or subwords, creating word embeddings that best represent the structure of the language.
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.
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models
We validate it using a dataset of images from 15 categories, each with five critic review texts and annotated rankings in both English and Japanese, totaling over 2, 000 data instances.
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 #228 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.
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 #13 on
Nested Named Entity Recognition
on ACE 2005
Zero Pronouns Identification based on Span prediction
In the experiments, our model surpassed the sequence labeling baseline.
Neural Machine Translation with Synchronous Latent Phrase Structure
It is reported that grammatical information is useful for machine translation (MT) task.
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.
