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KPA studies for arguments and reviews have been reported in the literature. | |
Our approach takes advantage of off-the-shelf retrievers (e.g., CLIP for retrieving images of book covers) or incorporate retriever-specific logic (e.g., date constraints). | |
By providing a unified interface for a predefined collection of newspapers, we aim to make Fundus broadly usable even for non-technical users. | |
We also investigate different contrastive designs to combine the strengths of the contrastive loss with teacher-student architectures used for distillation. | |
Recent advancements have predominantly been rooted either in semantic or syntactic methods. | |
Next, we analyze the vulnerability of the summarization systems and explore improving the robustness by data augmentation. | |
These issues hinder their trustworthiness and real-world applicability. | |
Each text element has two components: the semantic meaning and a linguistic realization. | |
In this paper, we demonstrate how knowledge transfer and adversarial training can be used to create efficient models capable of running on edge devices using a corpus of only several hours. | |
Artificial speech is then generated with a range of variants which have been captured in confusion matrices representing phoneme similarities. | |
The few-shot + translation system variants were submitted to the WebNLG 2023 shared task where they outperformed all other systems by substantial margins in all languages on all automatic metrics. | |
In discourse relation recognition, the classification labels are typically represented as one-hot vectors. | |
We have made the text generated by generative models publicly available. | |
Online peer counseling platforms enable conversations between millions of people seeking and offering mental health support. | |
Fine-tuning local NLP models is hindered by the skewed nature of real-world medical datasets, where rare findings represent a significant data imbalance. | |
These prompts integrate different techniques, including chain-of-thought reasoning and augmented knowledge. | |
However, previous approaches to this problem have focused on features of the content itself, and neglected potentially helpful insights from the reactions expressed by its online audience. | |
These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. | |
To address that, we introduce PrivaT5, a T5-based model that is further pre-trained on privacy policy text. | |
We present a new approach called MeritOpt based on the Personalized Federated Learning algorithm MeritFed that can be applied to Natural Language Tasks with heterogeneous data. | |
By utilizing four evaluation benchmarks, we investigate the controlled translation capability of LLMs in multiple dimensions and find that LLMs reach state-of-the-art performance in controlled translation. | |
These parameters can then be pruned for more efficient fine-tuning. | |
Since the argument extraction depends on the context from multiple sentences and the learning process is limited to very few examples, we find this novel task to be very challenging with substantively low performance. | |
However, these models are often domain specific. | |
Fine-tuning is a widely used technique for leveraging pre-trained language models (PLMs) in downstream tasks, but it can be computationally expensive and storage-intensive. | |
Experimental results indicate that DiffusPoll has achieved state-of-the-art performance in both the quality and diversity of poll generation tasks, and is more likely to hit the voices of minority. | |
It also avoids speech discretization in inference and is more robust to the DSU tokenization. | |
Empirical results indicate that distilling CANDLE on student models provides benefits across three downstream tasks. | |
Our experimental results demonstrate that RankMean outperforms existing baseline methods on multiple benchmarks. | |
However, the utilization of these models carries inherent risks, including but not limited to plagiarism, the dissemination of fake news, and issues in educational exercises. | |
This study addresses the challenges of learning unsupervised word representations for the morphologically rich and low-resource Ukrainian language. | |
It takes advantage of two complementary auto-encoding tasks: one reconstructs the input sentence on top of the [CLS] embedding; the other one predicts the bag-of-words feature of the input sentence based on the ordinary tokens' embeddings. | |
Through extensive experiments covering six summarization benchmarks, we show that high-quality extractive summaries can be assembled via approximating the outputs (abstractive summaries) of these generators. | |
Our resulting feature set shows comparable performance to much larger, semantically- and syntactically-based feature sets, supporting the linguistic value of orthographic and phonetic considerations in readability assessment. | |
We fine-tune a pre-trained machine translation model by the manually-aligned works of a particular translator. | |
To assess the impact of VSs on training data efficiency, we augment CDS data with different proportions of artificial VSs and use these datasets to train an auto-regressive model, GPT-2. | |
While recent advances have led to automated summarization solutions for legal documents, they typically provide generic summaries, which may not meet the diverse information needs of users. | |
Our method is evaluated on the MultiXScience dataset which includes scientific articles. | |
We generally divide multi-step reasoning into two phases: *path generation* to generate the reasoning path(s); and *answer calibration* post-processing the reasoning path(s) to obtain a final answer. | |
Subtask 1 revolves around the extraction of textual emotion-cause pairs, where causes are defined and annotated as textual spans within the conversation. | |
Despite their usefulness, writing expository text by hand is a challenging process that requires careful content planning, obtaining facts from multiple sources, and the ability to clearly synthesize these facts. | |
We created a multiple-choice question-and-answer (MCQA) exam by randomly sampling problems from standard LLM benchmarks. | |
This paper presents, to the best of our knowledge, the first comprehensive study of the complexities of product returns across a variety of e-commerce domains, focusing on the task of predicting the return reason. | |
Neural language models, which reach state-of-the-art results on most natural language processing tasks, are trained on large text corpora that inevitably contain value-burdened content and often capture undesirable biases, which the models reflect. | |
Our best performing models achieve an F1-score of 70.36 on development data and an F1-score of 66.13 on test data. | |
Finally, to guarantee reproducibility, we publicly release the code of our models and experiments. | |
Our findings can not only serve as a foundation for further research in the field but also provide a benchmark for assessing future advancements in automatic speech interruption detection. | |
For a lot of people, the internet have also become the only source of news and information about the world. | |
Extensive experiments on several widely used datasets demonstrate that DEMO achieves superior performance compared with various state-of-the-art methods. | |
Even if they have proven to be effective in detecting a wide variety of biases, metrics based on word embeddings lack transparency and interpretability. | |
Specifically, FEC needs to handle subtle nuance between labels, which can be complex and confusing. | |
Whether it be the integration of more refined models, the development of innovative pipelines, or user experience improvements, your contributions can propel this project to new heights. | |
We also tested our approach on existential (yes-no) questions with better results. | |
Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models. | |
In addition, we found that the intrinsic dimension of embeddings increases in the initial phases of training, indicating an expansion into higher-dimensional space. | |
Syntactic knowledge is invaluable information for many tasks which handle complex or long sentences, but typical pre-trained language models do not contain sufficient syntactic knowledge. | |
While previous research has only explored the first setting, the latter two are more representative of real-world applications. | |
We design two kinds of perturbation-based regularizers, including random-noise-based and adversarial-based. | |
To address these challenges, we propose McCrolin, a Multi-consistency Cross-lingual training framework, leveraging multi-task learning to enhance cross-lingual consistency, ranking stability, and input-size robustness. | |
Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. | |
To this end, we develop a prompt-based intent detection model in few-shot settings, which leverages the BERT original pre-training next sentence prediction task and the prompt template to detect the user`s intent. | |
Existing VSD work merely models the 2D geometrical vision features, thus inevitably falling prey to the problem of skewed spatial understanding of target objects. | |
To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. | |
In this paper, we propose a simple but effective method to enhance the extraction of structured evidence by leveraging the row and column semantics of tables. | |
Results show that our methods can achieve performance gains and enhance the performance and generalization ability of ABSA models. | |
Spanish is an official language in 20 countries; in 19 of them, it arrived by means of overseas colonisation. | |
As a result, the generated responses tend to be tedious, incoherent, and in lack of interactivity which means the degeneration problem is still unsolved. | |
Here, we present a prompt-based learning approach to transfer DAs from one domain, video games, to 7 new domains. | |
In this framework, people can propose new basic composition methods and combine them to get the new mixed composition methods. | |
To this end, we introduce a general post-training confidence calibration framework named RECAL to calibrate the predictive confidence of current machine learning models by employing graph neural networks to model the relations between different samples. | |
However, most research focuses on a limited set of resources, e.g., court opinions or oral arguments, for analyzing a specific perspective in court, e.g., partisanship or voting. | |
Based on this finding, we propose a gradient control method to consolidate the attack effect, comprising two strategies. | |
Here, we leverage dynamic epistemic logic to isolate a particular component of ToM and to generate controlled problems. | |
In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. | |
Also, self-attention-based models face the challenge of quadratic complexity with respect to sequence length. | |
To bring various modality configurations together, we constructed a benchmark for diverse-modal EL (DMEL) from existing EL datasets, covering all three modalities including text, image and table. | |
We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. | |
Unlike datasets that assume a small set of single-modality candidates, EDIS reflects real-world web image search scenarios by including a million multimodal image-text pairs as candidates. | |
Finally, we introduce a fine-grained, domain-agnostic evaluation method to assess hallucination in LLMs and promote responsible use. | |
In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. | |
The data has been created with minimal human intervention via an automated pipeline based on InstructBlip and GPT-3.5. | |
Extensive experiments on two real-world datasets show that CaM can effectively promote the model to learn causal relations and thus produce related work of higher quality and coherence. | |
An extensive number of annotation tools have been developed to facilitate the data labelling process. | |
However, in real-world scenarios, only the latest KB snapshot is available during training and as a result, the train dialogs may contain facts conflicting with the latest KB. | |
2016), we show that noun-classifier combinations are sensitive to same frequency, similarity, and co-occurrence interactions that structure gender systems. | |
We propose a model that extends event mentions with temporal commonsense inferences. | |
These findings contribute to efforts to develop robust multilingual benchmarks and improve LLM performance in diverse linguistic contexts. | |
We combine single-script features from Devanagari and Romanized Hindi Roberta using concatenation, addition, cross-attention, and convolutional networks. | |
We hypothesize that expanding GSR to follow the more liberal SRL text-based approach to action and participant identification could improve image comprehension results. | |
Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. | |
Lastly, to promote the open-source culture, we provide a collection of open-source libraries with the hope of facilitating future work in the field. | |
With Fabricator, we aim to support researchers in conducting reproducible dataset generation experiments using LLMs and help practitioners apply this approach to train models for downstream tasks. | |
PVGRU can perceive subtle semantic variability through summarizing variables that are optimized by two objectives we employ for training: distribution consistency and reconstruction. | |
We find that an NLI strategy of maximizing entailment improves text generation when the nucleus sampling randomness parameter value is high, while one which maximizes contradiction is in fact productive when the parameter value is low. | |
To further extend this task, we officially introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop questions with explicit reasoning steps in open-domain setting. | |
In this paper, we introduce and assess a set of measures PSentScore, aimed at quantifying the preservation of affective content in dialogue summaries. | |
Recent advances in neural theorem-proving resort to large language models and tree searches. | |
For the Argument Identification task, we employ the RoBERTa PLM with T5 position encoding for extraction tasks. | |
Our study concludes that while generating high-quality, semantically rich data might be within reach, the downstream utility of such generations remains to be seen, highlighting the outstanding challenges with automating linguistic annotation tasks. | |
In the assessment of this approach, we have employed the LaMP benchmark, specifically tailored for evaluating language models across diverse dimensions of personalization. |
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