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Incorporating target language semantic roles into a string-to-tree translation model

Frontiers of information Technology & Electronic Engineering 2017年第10期18卷 1534-1542页

作者： Chao SU Yu-hang GUO He-yan HUANG Shu-min SHI Chong FENG School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing 100081China Beijing Advanced Innovation Center for Imaging Technology Capital Normal UniversityBeijing 100048China

The string-to-tree model is one of the most successful syntax-based statistical machine translation(SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model:(1) adding role labels in the syntax tree;(2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.

关键词： Machine translation Semantic role Syntax tree String-to-tree

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Conference on Empirical Methods in Natural language processing, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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BIT at SemEval-2017 Task 1: Using Semantic information Space to Evaluate Semantic Textual Similarity 11

BIT at SemEval-2017 Task 1: Using Semantic Information Space...

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11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Wu, Hao Huang, Heyan Jian, Ping Guo, Yuhang Su, Chao Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9781945626555

This paper presents three systems for semantic textual similarity (STS) evaluation at SemEval-2017 STS task. One is an unsupervised system and the other two are supervised systems which simply employ the unsupervised one. All our systems mainly depend on the semantic information space (SIS), which is constructed based on the semantic hierarchical taxonomy in WordNet, to compute non-overlapping information content (IC) of sentences. Our team ranked 2nd among 31 participating teams by the primary score of Pearson correlation coefficient (PCC) mean of 7 tracks and achieved the best performance on Track 1 (AR-AR) dataset. © 2017 Association for Computational Linguistics

关键词： Semantics

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Concept pointer network for abstractive summarization 9

Concept pointer network for abstractive summarization

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2019 Conference on Empirical Methods in Natural language processing and 9th International Joint Conference on Natural language processing, EMNLP-IJCNLP 2019

作者： Wenbo, Wang Yang, Gao Heyan, Huang Yuxiang, Zhou School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9781950737901

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework. © 2019 Association for Computational Linguistics

关键词： Semantics

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Temporal enhanced sentence-level attention model for hashtag recommendation

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CAAI Transactions on Intelligence Technology 2018年第2期3卷 95-100页

作者： Jun Ma Chong Feng Ge Shi Xuewen Shi Heyang Huang Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications College of Computer Science Beijing Institute of Technology University 5 South Zhong Guan Cun Street of Haidian District Beijing People's Republic of China

Hashtags of microblogs can provide valuable information for many natural language processing tasks. How to recommend reliable hashtags automatically has attracted considerable attention. However, existing studies assumed that all the training corpus crawled from social networks are labelled correctly, while large sample statistics on real social media shows that there are 8.9% of microblogs with hashtags having wrong labels. The notable influence of noisy data to the classifier is ignored before. Meanwhile, recency also plays an important role in microblog hashtag, but the information is not used in the existing studies. Some temporal hashtags such as World Cup will ignite at a particular time, but at other times, the number of people talking about it will sharply decrease. To address the twofold shortcomings above, the authors propose an long short-term memory-based model, which uses temporal enhanced selective sentence-level attention to reduce the influence of wrong labelled microblogs to the classifier. Experimental results using a dataset of 1.7 million microblogs collected from SINA Weibo microblogs demonstrated that the proposed method could achieve significantly better performance than the state-of-the-art methods.

关键词：分类器数据集计算机技术智能技术

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Vertical classification of web pages for structured data extraction

Vertical classification of web pages for structured data ext...

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8th Asia information Retrieval Societies Conference, AIRS 2012

作者： Li, Long Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9783642353406

We propose a general hierarchical vertical classification framework, which can automatically discover the inherent hierarchical structure of relationships among verticals based on flat datasets, and then build a hierarchical classifier. We conducted a set of comparison experiments to verify the performance of it, such as with flat vs hierarchical structure of relationships, as well as among different feature selection and classification methods. Experimental results show that the hierarchical classifiers built on the basis of the proposed framework make big improvements over the flat classifiers when classifying unseen web pages. Among them, the Support Vector Machine using Odds Ratio to select discriminative features performs best. © Springer-Verlag 2012.

关键词： Classification (of information)

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Augmenting Context Representation with Triggers Knowledge for Relation Extraction 12th

Augmenting Context Representation with Triggers Knowledge fo...

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12th IFIP TC 12 International Conference on Intelligent information processing, IIP 2022

作者： Li, En Shi, Shumin Yang, Zhikun Huang, He Yan School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031039478

Relation Extraction (RE) requires the model to classify the correct relation from a set of relation candidates given the corresponding sentence and two entities. Recent work mainly studies how to utilize more data or incorporate extra context information especially with Pre-trained language Models (PLMs). However, these models still face with the challenges of avoiding being affected by irrelevant or misleading words. In this paper, we propose a novel model to help alleviate such deficiency. Specifically, our model automatically mines the triggers of the sentence iteratively with the sentence itself from the previous iteration, and augment the semantics of the context representation from BERT with both entity pair and triggers skillfully. We conduct extensive experiments to evaluate the proposed model and effectively obtain empirical improvement in TACRED. © 2022, IFIP International Federation for information processing.

关键词： Semantics

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Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension 7

Incorporating Option and Out-of-domain Knowledge for Multi-c...

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7th IEEE International Conference on cloud computing and Intelligence Systems, CCIS 2021

作者： Xu, Yuan Shi, Shumin Huang, Heyan Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9781665441490

Multi-choice Machine Reading Comprehension (MRC) requires the model to select the correct answer from a set of answer candidates given the corresponding passage and question. Previous studies mainly focus on complex matching networks to model the relationship among options, passage and question. However, these models obtain little improvement over the powerful Pre-trained language Models (PLMs). In this paper, we propose a simple method to incorporate option knowledge from PLMs and introduce out-of-domain knowledge by multi-task learning skillfully. Our approach obtains state-of-the-art results on Chinese multi-choice MRC dataset ReCO and also effectively improves the performance on C3. © 2021 IEEE.

关键词： Domain Knowledge

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Deep Hierarchical Attention Flow for Visual Commonsense Reasoning 9th

Deep Hierarchical Attention Flow for Visual Commonsense Reas...

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9th CCF International Conference on Natural language processing and Chinese computing, NLPCC 2020

作者： Song, Yuansheng Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Bejing China

ISBN: (纸本)9783030604493

Visual Commonsense Reasoning (VCR) requires a thoroughly understanding general information connecting language and vision, as well as the background world knowledge. In this paper, we introduce a novel yet powerful deep hierarchical attention flow framework, which takes full advantage of text information in the query and candidate responses to perform reasoning over the image. Moreover, inspired by the success of machine reading comprehension, we also model the correlation among candidate responses to obtain better response representations. Extensive quantitative and qualitative experiments are conducted to evaluate the proposed model. Empirical results on the benchmark VCR1.0 show that the proposed model outperforms existing strong baselines, which demonstrates the effectiveness of our method. © 2020, Springer Nature Switzerland AG.

关键词： Visual languages

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent information processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for information processing 2024.

关键词： Sentiment analysis

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