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  1. Article ; Online: Machine vision model for detection of foreign substances at the bottom of empty large volume parenteral.

    Yuan, Pi / Li, Chen / Tang, Peng / Yuan, Bin / Yin, Yongjing

    PloS one

    2024  Volume 19, Issue 4, Page(s) e0298108

    Abstract: Empty large volume parenteral (LVP) bottle has irregular shape and narrow opening, and its detection accuracy of the foreign substances at the bottom is higher than that of ordinary packaging bottles. The current traditional detection method for the ... ...

    Abstract Empty large volume parenteral (LVP) bottle has irregular shape and narrow opening, and its detection accuracy of the foreign substances at the bottom is higher than that of ordinary packaging bottles. The current traditional detection method for the bottom of LVP bottles is to directly use manual visual inspection, which involves high labor intensity and is prone to visual fatigue and quality fluctuations, resulting in limited applicability for the detection of the bottom of LVP bottles. A geometric constraint-based detection model (GCBDM) has been proposed, which combines the imaging model and the shape characteristics of the bottle to construct a constraint model of the imaging parameters, according to the detection accuracy and the field of view. Then, the imaging model is designed and optimized for the detection. Further, the generalized GCBDM has been adopted to different bottle bottom detection scenarios, such as cough syrup and capsule medicine bottles by changing the target parameters of the model. The GCBDM, on the one hand, can avoid the information at the bottom being blocked by the narrow opening in the imaging optical path. On the other hand, by calculating the maximum position deviation between the center of visual inspection and the center of the bottom, it can provide the basis for the accuracy design of the transmission mechanism in the inspection, thus further ensuring the stability of the detection.
    MeSH term(s) Drug Packaging/methods ; Humans ; Models, Theoretical
    Language English
    Publishing date 2024-04-26
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0298108
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Improving Machine Translation with Large Language Models

    Zeng, Jiali / Meng, Fandong / Yin, Yongjing / Zhou, Jie

    A Preliminary Study with Cooperative Decoding

    2023  

    Abstract: Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior ... ...

    Abstract Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior translation quality. Therefore, it is crucial to understand in which scenarios LLMs outperform traditional NMT systems and how to leverage their strengths. In this paper, we first conduct a comprehensive analysis to assess the strengths and limitations of various commercial NMT systems and MT-oriented LLMs. Our findings indicate that neither NMT nor MT-oriented LLMs alone can effectively address all the translation issues, but MT-oriented LLMs can serve as a promising complement to the NMT systems. Building upon these insights, we explore hybrid methods and propose Cooperative Decoding (CoDec), which treats NMT systems as a pretranslation model and MT-oriented LLMs as a supplemental solution to handle complex scenarios beyond the capability of NMT alone. The results on the WMT22 test sets and a newly collected test set WebCrawl demonstrate the effectiveness and efficiency of CoDec, highlighting its potential as a robust solution for combining NMT systems with MT-oriented LLMs in machine translation.
    Keywords Computer Science - Computation and Language
    Subject code 410
    Publishing date 2023-11-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: TIM

    Zeng, Jiali / Meng, Fandong / Yin, Yongjing / Zhou, Jie

    Teaching Large Language Models to Translate with Comparison

    2023  

    Abstract: Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks that require more specialized knowledge such as translation. One possible ... ...

    Abstract Open-sourced large language models (LLMs) have demonstrated remarkable efficacy in various tasks with instruction tuning. However, these models can sometimes struggle with tasks that require more specialized knowledge such as translation. One possible reason for such deficiency is that instruction tuning aims to generate fluent and coherent text that continues from a given instruction without being constrained by any task-specific requirements. Moreover, it can be more challenging for tuning smaller LLMs with lower-quality training data. To address this issue, we propose a novel framework using examples in comparison to teach LLMs to learn translation. Our approach involves presenting the model with examples of correct and incorrect translations and using a preference loss to guide the model's learning. We evaluate our method on WMT2022 test sets and show that it outperforms existing methods. Our findings offer a new perspective on fine-tuning LLMs for translation tasks and provide a promising solution for generating high-quality translations. Please refer to Github for more details: https://github.com/lemon0830/TIM.
    Keywords Computer Science - Computation and Language
    Subject code 004
    Publishing date 2023-07-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding

    Zeng, Jiali / Yin, Yongjing / Jiang, Yufan / Wu, Shuangzhi / Cao, Yunbo

    2022  

    Abstract: Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel ... ...

    Abstract Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.

    Comment: Findings of EMNLP 2022
    Keywords Computer Science - Computation and Language
    Subject code 401
    Publishing date 2022-11-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Explicit Syntactic Guidance for Neural Text Generation

    Li, Yafu / Cui, Leyang / Yan, Jianhao / Yin, Yongjing / Bi, Wei / Shi, Shuming / Zhang, Yue

    2023  

    Abstract: Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the ... ...

    Abstract Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. The decoding process can be decomposed into two parts: (1) predicting the infilling texts for each constituent in the lexicalized syntax context given the source sentence; (2) mapping and expanding each constituent to construct the next-level syntax context. Accordingly, we propose a structural beam search method to find possible syntax structures hierarchically. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines, while also demonstrating effectiveness in terms of interpretability, controllability, and diversity.

    Comment: ACL 2023
    Keywords Computer Science - Computation and Language
    Subject code 430
    Publishing date 2023-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: AI-driven platform for systematic nomenclature and intelligent knowledge acquisition of natural medicinal materials

    Yang, Zijie / Yin, Yongjing / Kong, Chaojun / Chi, Tiange / Tao, Wufan / Zhang, Yue / Xu, Tian

    2023  

    Abstract: Natural Medicinal Materials (NMMs) have a long history of global clinical applications, accompanied by extensive informational records. Despite their significant impact on healthcare, the field faces a major challenge: the non-standardization of NMM ... ...

    Abstract Natural Medicinal Materials (NMMs) have a long history of global clinical applications, accompanied by extensive informational records. Despite their significant impact on healthcare, the field faces a major challenge: the non-standardization of NMM knowledge, stemming from historical complexities and causing limitations in broader applications. To address this, we introduce a Systematic Nomenclature for NMMs, underpinned by ShennongAlpha, an AI-driven platform designed for intelligent knowledge acquisition. This nomenclature system enables precise identification and differentiation of NMMs. ShennongAlpha, cataloging over ten thousand NMMs with standardized bilingual information, enhances knowledge management and application capabilities, thereby overcoming traditional barriers. Furthermore, it pioneers AI-empowered conversational knowledge acquisition and standardized machine translation. These synergistic innovations mark the first major advance in integrating domain-specific NMM knowledge with AI, propelling research and applications across both NMM and AI fields while establishing a groundbreaking precedent in this crucial area.

    Comment: 55 pages, 7 figures, 10 supplementary figures, 1 table, 1 supplementary table
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Databases ; Computer Science - Information Retrieval
    Subject code 004
    Publishing date 2023-12-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Exploring Discriminative Word-Level Domain Contexts for Multi-Domain Neural Machine Translation.

    Su, Jinsong / Zeng, Jiali / Xie, Jun / Wen, Huating / Yin, Yongjing / Liu, Yang

    IEEE transactions on pattern analysis and machine intelligence

    2021  Volume 43, Issue 5, Page(s) 1530–1545

    Abstract: Owing to its practical significance, multi-domain Neural Machine Translation (NMT) has attracted much attention recently. Recent studies mainly focus on constructing a unified NMT model with mixed-domain training corpora to switch translation between ... ...

    Abstract Owing to its practical significance, multi-domain Neural Machine Translation (NMT) has attracted much attention recently. Recent studies mainly focus on constructing a unified NMT model with mixed-domain training corpora to switch translation between different domains. In these models, the words in the same sentence are not well distinguished, while intuitively, they are related to the sentence domain to varying degrees and thus should exert different effects on the multi-domain NMT model. In this article, we are committed to distinguishing and exploiting different word-level domain contexts for multi-domain NMT. For this purpose, we adopt multi-task learning to jointly model NMT and monolingual attention-based domain classification tasks, improving the NMT model in two ways: 1) One domain classifier and one adversarial domain classifier are introduced to conduct domain classifications of input sentences. During this process, two generated gating vectors are used to produce domain-specific and domain-shared annotations for decoder; 2) We equip decoder with an attentional domain classifier. Then, the derived attentional weights are utilized to refine the model training via word-level cost weighting, so that the impacts of target words can be discriminated by their relevance to sentence domain. Experimental results on several multi-domain translations demonstrate the effectiveness of our model.
    Language English
    Publishing date 2021-04-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2019.2954406
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Inter-library loan at ZB MED

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  8. Book ; Online: DualNER

    Zeng, Jiali / Jiang, Yufan / Yin, Yongjing / Wang, Xu / Lin, Binghuai / Cao, Yunbo

    A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition

    2022  

    Abstract: We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary ... ...

    Abstract We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. After obtaining a sufficient NER model trained on the source data, we further train it on the target data in a {\it dual-teaching} manner, in which the pseudo-labels for one task are constructed from the prediction of the other task. Moreover, based on the span prediction, an entity-aware regularization is proposed to enhance the intrinsic cross-lingual alignment between the same entities in different languages. Experiments and analysis demonstrate the effectiveness of our DualNER. Code is available at https://github.com/lemon0830/dualNER.

    Comment: Findings of EMNLP 2022
    Keywords Computer Science - Computation and Language
    Subject code 410
    Publishing date 2022-11-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Soft Language Clustering for Multilingual Model Pre-training

    Zeng, Jiali / Jiang, Yufan / Yin, Yongjing / Jing, Yi / Meng, Fandong / Lin, Binghuai / Cao, Yunbo / Zhou, Jie

    2023  

    Abstract: Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is ... ...

    Abstract Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME including text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer.
    Keywords Computer Science - Computation and Language
    Subject code 410
    Publishing date 2023-06-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Iterative Dual Domain Adaptation for Neural Machine Translation

    Zeng, Jiali / Liu, Yang / Su, Jinsong / Ge, Yubin / Lu, Yaojie / Yin, Yongjing / Luo, Jiebo

    2019  

    Abstract: Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the ... ...

    Abstract Previous studies on the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of-domain translation knowledge to in-domain NMT model. In this paper, we argue that such a strategy fails to fully extract the domain-shared translation knowledge, and repeatedly utilizing corpora of different domains can lead to better distillation of domain-shared translation knowledge. To this end, we propose an iterative dual domain adaptation framework for NMT. Specifically, we first pre-train in-domain and out-of-domain NMT models using their own training corpora respectively, and then iteratively perform bidirectional translation knowledge transfer (from in-domain to out-of-domain and then vice versa) based on knowledge distillation until the in-domain NMT model convergences. Furthermore, we extend the proposed framework to the scenario of multiple out-of-domain training corpora, where the above-mentioned transfer is performed sequentially between the in-domain and each out-of-domain NMT models in the ascending order of their domain similarities. Empirical results on Chinese-English and English-German translation tasks demonstrate the effectiveness of our framework.

    Comment: EMNLP2019
    Keywords Computer Science - Computation and Language
    Subject code 400
    Publishing date 2019-12-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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