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检索条件"任意字段=Conference on empirical methods in natural language processing"

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GOME: Grounding-based Metaphor Binding With Conceptual Elaboration For Figurative language Illustration

GOME: Grounding-based Metaphor Binding With Conceptual Elabo...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Zhang, Linhao Liu, Jintao Jin, Li Wang, Hao Wei, Kaiwen Xu, Guangluan Aerospace Information Research Institute Chinese Academy of Sciences China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences China North China University of Technology China Chongqing University China

ISBN: (纸本)9798891761643

The illustration or visualization of figurative language, such as linguistic metaphors, is an emerging challenge for existing Large language Models (LLMs) and multimodal *** to their comparison of seemingly unrelated concepts in metaphors, existing LLMs have a tendency of over-literalization, which illustrates figurative language solely based on literal objects, ignoring the underlying groundings and associations across disparate metaphorical ***, prior approaches have ignored the binding process between visual objects and metaphorical attributes, which further intensifies the infidelity of visual *** address the issues above, we propose GOME (GrOunding-based MEtaphor Binding), which illustrates linguistic metaphors from the grounding perspective elaborated through *** consists of two steps for metaphor illustration, including grounding-based elaboration and scenario *** the elaboration step, metaphorical knowledge is integrated into systematic instructions for LLMs, which employs a CoT prompting method rooted in *** approach specifies metaphorical devices such as vehicles and groundings, to ensure accurate and faithful descriptions consumed by text-to-image *** the visualization step, an inference-time metaphor binding method is realized based on elaboration outputs, which register attentional control during the diffusion process, and captures the underlying attributes from the abstract metaphorical *** evaluations using multiple downstream tasks confirm that, GOME is superior to isolated LLMs, diffusion models, or their direct collaboration. © 2024 Association for Computational Linguistics.

关键词： Visualization

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Learning Knowledge-Enhanced Contextual language Representations for Domain natural language Understanding

Learning Knowledge-Enhanced Contextual Language Representati...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Zhang, Taolin Xu, Ruyao Wang, Chengyu Duan, Zhongjie Chen, Cen Qiu, Minghui Cheng, Dawei He, Xiaofeng Qian, Weining East China Normal Univ Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9798891760608

Knowledge-Enhanced Pre-trained language Models (KEPLMs) improve the performance of various downstream NLP tasks by injecting knowledge facts from large-scale Knowledge Graphs (KGs). However, existing methods for pre-training KEPLMs with relational triples are difficult to be adapted to close domains due to the lack of sufficient domain graph semantics. In this paper, we propose a Knowledgeenhanced language Representation learning framework for various clOsed dOmains (KAN-GAROO) via capturing the implicit graph structure among the entities. Specifically, since the entity coverage rates of closed-domain KGs can be relatively low and may exhibit the global sparsity phenomenon for knowledge injection, we consider not only the shallow relational representations of triples but also the hyperbolic embeddings of deep hierarchical entityclass structures for effective knowledge fusion. Moreover, as two closed-domain entities under the same entity-class often have locally dense neighbor subgraphs counted by max point bi-connected component, we further propose a data augmentation strategy based on contrastive learning over subgraphs to construct hard negative samples of higher quality. It makes the underlying KELPMs better distinguish the semantics of these neighboring entities to further complement the global semantic sparsity. In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.

关键词： Knowledge graph

来源：评论

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Unleashing the Power of Large language Models in Zero-shot Relation Extraction via Self-Prompting

Unleashing the Power of Large Language Models in Zero-shot R...

引用

2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Liu, Siyi Li, Yang Li, Jiang Yang, Shan Lan, Yunshi EPFL Lausanne Switzerland MYBank Ant Group Beijing China Beihang University Beijing China East China Normal University Beijing China

ISBN: (纸本)9798891761681

Recent research in zero-shot Relation Extraction (RE) has focused on using Large language Models (LLMs) due to their impressive zero-shot capabilities. However, current methods often perform suboptimally, mainly due to a lack of detailed, context-specific prompts needed for understanding various sentences and relations. To address this, we introduce the Self-Prompting framework, a novel method designed to fully harness the embedded RE knowledge within LLMs. Specifically, our framework employs a three-stage diversity approach to prompt LLMs, generating multiple synthetic samples that encapsulate specific relations from scratch. These generated samples act as in-context learning samples, offering explicit and context-specific guidance to efficiently prompt LLMs for RE. Experimental evaluations on benchmark datasets show our approach outperforms existing LLM-based zero-shot RE methods. Additionally, our experiments confirm the effectiveness of our generation pipeline in producing high-quality synthetic data that enhances performance. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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LM-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided language Model Exploitation

LM-Interview: An Easy-to-use Smart Interviewer System via Kn...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Li, Hanming Yu, Jifan Li, Ruimiao Hao, Zhanxin Yan, Xuan Yuan, Jiaxin Xu, Bin Li, Juanzi Liu, Zhiyuan Department of Computer Science and Technology BNRist Tsinghua Univeristy China Tsinghua University China

ISBN: (纸本)9798891761674

Semi-structured interviews are a crucial method of data acquisition in qualitative research. Typically controlled by the interviewer, the process progresses through a question-and-answer format, aimed at eliciting information from the interviewee. However, interviews are highly time-consuming and demand considerable experience of the interviewers, which greatly limits the efficiency and feasibility of data collection. Therefore, we introduce LM-Interview, a novel system designed to automate the process of preparing, conducting and analyzing semi-structured interviews. Experimental results demonstrate that LM-Interview achieves performance comparable to that of skilled human interviewers. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer

Nearest Neighbor Machine Translation is Meta-Optimizer on Ou...

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conference on empirical methods in natural language processing (EMNLP)

作者： Gaol, Ruize Zhang, Zhirui Dui, Yichao Liu, Lemao Wang, Rui Shanghai Jiao Tong Univ Shanghai Peoples R China Tencent AI Lab Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798891760608

Nearest Neighbor Machine Translation (kNN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval. However, the reasons underlying its success have not been thoroughly investigated. In this paper, we comprehensively analyze kNN-MT through theoretical and empirical studies. Initially, we provide new insights into the working mechanism of kNN-MT as an efficient technique to implicitly execute gradient descent on the output projection layer of NMT, indicating that it is a specific case of model fine-tuning. Subsequently, we conduct multi-domain experiments and word-level analysis to examine the differences in performance between kNN-MT and entire-model fine-tuning. Our findings suggest that: (i) Incorporating kNNMT with adapters yields comparable translation performance to fine-tuning on in-domain test sets, while achieving better performance on out-of-domain test sets;(ii) Fine-tuning significantly outperforms kNN-MT on the recall of in-domain low-frequency words, but this gap could be bridged by optimizing the context representations with additional adapter layers.

关键词： Computer aided language translation

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Goal-Driven Explainable Clustering via language Descriptions

Goal-Driven Explainable Clustering via Language Descriptions

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Wang, Zihan Shang, Jingbo Zhong, Ruiqi

ISBN: (纸本)9798891760608

Unsupervised clustering is widely used to explore large corpora, but existing formulations neither consider the users' goals nor explain clusters' meanings. We propose a new task formulation, "Goal-Driven Clustering with Explanations" (GOALEX), which represents both the goal and the explanations as free-form language descriptions. For example, to categorize the errors made by a summarization system, the input to GOALEX is a corpus of annotator-written comments for system-generated summaries and a goal "cluster the comments based on why the annotators think the summary is imperfect.";the outputs are text clusters each with an explanation ("this cluster mentions that the summary misses important context information."), which relates to the goal and accurately explains which comments should (not) belong to a cluster. To tackle GOALEX, we prompt a language model with "[corpus subset] + [goal] + Brainstorm a list of explanations each representing a cluster.";then we classify whether each sample belongs to a cluster based on its explanation;finally, we use integer linear programming to select a subset of candidate clusters to cover most samples while minimizing overlaps. Under both automatic and human evaluation on corpora with or without labels, our method produces more accurate and goal-related explanations than prior methods.

关键词： Integer programming

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Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect language Models

Navigating the Grey Area: How Expressions of Uncertainty and...

引用

conference on empirical methods in natural language processing (EMNLP)

作者： Zhou, Kaitlyn Jurafsky, Dan Hashimoto, Tatsunori Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798891760608

The increased deployment of LMs for real-world tasks involving knowledge and facts makes it important to understand model epistemology: what LMs think they know, and how their attitudes toward that knowledge are affected by language use in their inputs. Here, we study an aspect of model epistemology: how epistemic markers of certainty, uncertainty, or evidentiality like "I'm sure it's", "I think it's", or "Wikipedia says it's" affect models, and whether they contribute to model failures. We develop a typology of epistemic markers and inject 50 markers into prompts for question answering. We find that LMs are highly sensitive to epistemic markers in prompts, with accuracies varying more than 80%. Surprisingly, we find that expressions of high certainty result in a 7% decrease in accuracy as compared to low certainty expressions;similarly, factive verbs hurt performance, while evidentials benefit performance. Our analysis of a popular pretraining dataset shows that these markers of uncertainty are associated with answers on question-answering websites, while markers of certainty are associated with questions. These associations may suggest that the behavior of LMs is based on mimicking observed language use, rather than truly reflecting epistemic uncertainty.

关键词： Uncertainty analysis

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ExpertEase: A Multi-Agent Framework for Grade-Specific Document Simplification with Large language Models

ExpertEase: A Multi-Agent Framework for Grade-Specific Docum...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Mo, Kaijie Hu, Renfen School of International Chinese Language Education Beijing Normal University China

ISBN: (纸本)9798891761681

Text simplification is crucial for making texts more accessible, yet current research primarily focuses on sentence-level simplification, neglecting document-level simplification and the different reading levels of target *** bridge these gaps, we introduce ExpertEase, a multi-agent framework for grade-specific document simplification using Large language *** simulates real-world text simplification by introducing expert, teacher, and student agents that cooperate on the task and rely on external tools for *** demonstrate that this multi-agent approach significantly enhances LLMs' ability to simplify reading materials for diverse ***, we evaluate the performance of LLMs varying in size and type, and compare LLM-generated texts with human-authored ones, highlighting their potential in educational resource development and guiding future research. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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IndoCL: Benchmarking Indonesian language Development Assessment

IndoCL: Benchmarking Indonesian Language Development Assessm...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Lin, Nankai Wu, Hongyan Zheng, Weixiong Liao, Xingming Jiang, Shengyi Yang, Aimin Xiao, Lixian School of Computer Science and Technology Guangdong University of Technology China College of Computer National University of Defense Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China Faculty of Asian Languages and Cultures Guangdong University of Foreign Studies China

ISBN: (纸本)9798891761681

Recently, the field of language acquisition (LA) has significantly benefited from natural language processing technologies. A crucial task in LA involves tracking the evolution of language learners' competence, namely language development assessment (LDA). However, the majority of LDA research focuses on high-resource languages, with limited attention directed toward low-resource languages. Moreover, existing methodologies primarily depend on linguistic rules and language characteristics, with a limited exploration of exploiting pre-trained language models (PLMs) for LDA. In this paper, we construct the IndoCL corpus (Indonesian Corpus of L2 Learners), which comprises compositions written by undergraduate students majoring in Indonesian language. Moreover, we propose a model for LDA tasks, which automatically extracts language-independent features, relieving laborious computation and reliance on specific language. The proposed model uses sequential information attention and similarity representation learning to capture the differences and common information from the first-written and second-written essays, respectively. It has demonstrated remarkable performance on both our self-constructed corpus and publicly available corpora. Our work could serve as a novel benchmark for Indonesian LDA tasks. We also explore the feasibility of using existing large-scale language models (LLMs) for LDA tasks. The results show significant potential for improving LLM performance in LDA tasks. © 2024 Association for Computational Linguistics.

关键词： Students

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Document-Level Machine Translation with Large language Models

Document-Level Machine Translation with Large Language Model...

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conference on empirical methods in natural language processing (EMNLP)

作者： Wang, Longyue Lyu, Chenyang Ji, Tianbo Zhang, Zhirui Yu, Dian Shi, Shuming Tu, Zhaopeng Tencent AI Lab Shenzhen Guangdong Peoples R China MBZUAI Abu Dhabi U Arab Emirates Dublin City Univ Dublin Ireland

ISBN: (纸本)9798891760608

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking documentlevel machine translation (MT) as a testbed, this paper provides an in-depth evaluation of LLMs' ability on discourse modeling. The study focuses on three aspects: 1) Effects of Context-Aware Prompts, where we investigate the impact of different prompts on document-level translation quality and discourse phenomena;2) Comparison of Translation Models, where we compare the translation performance of ChatGPT with commercial MT systems and advanced document-level MT methods;3) Analysis of Discourse Modelling Abilities, where we further probe discourse knowledge encoded in LLMs and shed light on impacts of training techniques on discourse modeling. By evaluating on a number of benchmarks, we surprisingly find that LLMs have demonstrated superior performance and show potential to become a new paradigm for document-level translation: 1) leveraging their powerful long-text modeling capabilities, GPT-3.5 and GPT-4 outperform commercial MT systems in terms of human evaluation;(1) 2) GPT-4 demonstrates a stronger ability for probing linguistic knowledge than GPT-3.5. This work highlights the challenges and opportunities of LLMs for MT, which we hope can inspire the future design and evaluation of LLMs.(2)

关键词： Machine translation

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