Search Results for author:
Found 56 papers, 19 papers with code
Top-Down RST Parsing Utilizing Granularity Levels in Documents
To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures.
Ranked #3 on
Discourse Parsing
on RST-DT
Syntactically Look-Ahead Attention Network for Sentence Compression
Sentence compression is the task of compressing a long sentence into a short one by deleting redundant words.
Ranked #1 on
Sentence Compression
on Google Dataset
A Simple and Strong Baseline for End-to-End Neural RST-style Discourse Parsing
To promote and further develop RST-style discourse parsing models, we need a strong baseline that can be regarded as a reference for reporting reliable experimental results.
Ranked #1 on
Discourse Parsing
on Instructional-DT (Instr-DT)
Towards Table-to-Text Generation with Numerical Reasoning
In summary, our contributions are (1) a new dataset for numerical table-to-text generation using pairs of a table and a paragraph of a table description with richer inference from scientific papers, and (2) a table-to-text generation framework enriched with numerical reasoning.
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
To solve this problem, we theoretically analyzed NS loss to assist hyperparameter tuning and understand the better use of the NS loss in KGE learning.
SODA: Story Oriented Dense Video Captioning Evaluation Framework
This paper proposes a new evaluation framework, Story Oriented Dense video cAptioning evaluation framework (SODA), for measuring the performance of video story description systems.
Character-based Thai Word Segmentation with Multiple Attentions
These models estimate word boundaries from a character sequence.
Ranked #2 on
Thai Word Segmentation
on BEST-2010
Generating Weather Comments from Meteorological Simulations
The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users.
Unified Interpretation of Softmax Cross-Entropy and Negative Sampling: With Case Study for Knowledge Graph Embedding
In knowledge graph embedding, the theoretical relationship between the softmax cross-entropy and negative sampling loss functions has not been investigated.
Ranked #14 on
Link Prediction
on FB15k-237
Bidirectional Transformer Reranker for Grammatical Error Correction
Pre-trained seq2seq models have achieved state-of-the-art results in the grammatical error correction task.
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.
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.
An Empirical Study of Building a Strong Baseline for Constituency Parsing
This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing.
Ranked #16 on
Constituency Parsing
on Penn Treebank
Can we obtain significant success in RST discourse parsing by using Large Language Models?
Recently, decoder-only pre-trained large language models (LLMs), with several tens of billion parameters, have significantly impacted a wide range of natural language processing (NLP) tasks.
LATTE: Lattice ATTentive Encoding for Character-based Word Segmentation
Our model employs the lattice structure to handle segmentation alternatives and utilizes graph neural networks along with an attention mechanism to attentively extract multi-granularity representation from the lattice for complementing character representations.
Ranked #1 on
Chinese Word Segmentation
on CTB6
(using extra training data)
A Simple and Effective Usage of Word Clusters for CBOW Model
We propose a simple and effective method for incorporating word clusters into the Continuous Bag-of-Words (CBOW) model.
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.
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries
This paper tackles automation of the pyramid method, a reliable manual evaluation framework.
Higher-Order Syntactic Attention Network for Longer Sentence Compression
To solve this problem, we propose a higher-order syntactic attention network (HiSAN) that can handle higher-order dependency features as an attention distribution on LSTM hidden states.
Ranked #3 on
Sentence Compression
on Google Dataset
Supervised Attention for Sequence-to-Sequence Constituency Parsing
The sequence-to-sequence (Seq2Seq) model has been successfully applied to machine translation (MT).
Context-aware Neural Machine Translation with Coreference Information
We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly.
A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models
Our injection method can also be used together with previous methods.
Split or Merge: Which is Better for Unsupervised RST Parsing?
The first one builds the optimal tree in terms of a dissimilarity score function that is defined for splitting a text span into smaller ones.
Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization
To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document.
Neural text normalization leveraging similarities of strings and sounds
We propose neural models that can normalize text by considering the similarities of word strings and sounds.
Pointing to Subwords for Generating Function Names in Source Code
We tackle the task of automatically generating a function name from source code.
Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers
Numerical tables are widely used to present experimental results in scientific papers.
Improving Neural RST Parsing Model with Silver Agreement Subtrees
We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT.
Ranked #2 on
Discourse Parsing
on RST-DT
(using extra training data)
An Empirical Study of Generating Texts for Search Engine Advertising
Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain.
One-class Text Classification with Multi-modal Deep Support Vector Data Description
This work presents multi-modal deep SVDD (mSVDD) for one-class text classification.
A New Surprise Measure for Extracting Interesting Relationships between Persons
We therefore propose a method for extracting interesting relationships between persons from natural language texts by focusing on their surprisingness.
Hierarchical Trivia Fact Extraction from Wikipedia Articles
Recently, automatic trivia fact extraction has attracted much research interest.
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling
On the other hand, properties of the NS loss function that are considered important for learning, such as the relationship between the noise distribution and the number of negative samples, have not been investigated theoretically.
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing
Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence.
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer
Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents.
Ranked #4 on
Extractive Text Summarization
on CNN / Daily Mail
Improving Character-Aware Neural Language Model by Warming up Character Encoder under Skip-gram Architecture
Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology.
Making Your Tweets More Fancy: Emoji Insertion to Texts
The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction.
Abstractive Document Summarization with Word Embedding Reconstruction
Neural sequence-to-sequence (Seq2Seq) models and BERT have achieved substantial improvements in abstractive document summarization (ADS) without and with pre-training, respectively.
Generic Mechanism for Reducing Repetitions in Encoder-Decoder Models
Encoder-decoder models have been commonly used for many tasks such as machine translation and response generation.
Aspect-based Analysis of Advertising Appeals for Search Engine Advertising
Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising.
Generating Repetitions with Appropriate Repeated Words
A repetition is a response that repeats words in the previous speaker's utterance in a dialogue.
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization
Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them.
Subsampling for Knowledge Graph Embedding Explained
In this article, we explain the recent advance of subsampling methods in knowledge graph embedding (KGE) starting from the original one used in word2vec.
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.
Generating Diverse Translation with Perturbed kNN-MT
Generating multiple translation candidates would enable users to choose the one that satisfies their needs.
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.
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.
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.
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.
Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences
NLP systems are on par or, in some cases, better than humans at accomplishing specific tasks.
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.
