Reviewerly/exHarmony huggingface dataset:
Role of tacit knowledge management process and innovation capability for stimulating organizational performance: empirical analysis, PLS-SEM approach

Purpose This article aims to explore the connections between tacit knowledge management and the capacity to create new products and services for stimulating organizational performance. Design/methodology/approach This research utilizes a questionnaire-based study and 378 questionnaires gathered from different provinces of China between August and October 2022. The SmartPLS technique was used to evaluate the regression and mediation analysis on lower-order and higher-order components of the research hypotheses behind the model. Findings This investigation's results indicate that the tacit knowledge management process (TKMP) significantly drives product and service innovation and impacts organizational performance (ORP). According to the results, TKMP did not directly influence ORP and product innovation to mediate between Tacit knowledge and organizational performance. Research limitations/implications Future research should concentrate on different combinations of influences on innovation and other consequences of introducing innovation into businesses. Moreover, researchers may add moderators to innovation and organizational performance. Practical implications This study assists managers in how tacit knowledge management affects organisational performance by examining product/service innovation capabilities. Product innovation also mediates between tacit knowledge and organizational performance. Service innovation improves organizational performance, prioritizing knowledge creation, sharing and retention to increase innovation and organizational success. Originality/value This study contributes to the literature on tacit knowledge management, innovation capability and organizational performance by concentrating on the tacit knowledge process and using the resource-based view. This study gives a solid theoretical and practical basis for understanding the component interactions.
Mining Career Patterns from Job Portals with Relevance to Oman

The overall economy of a country improves with the increase in turnover and sustainability of its business establishments. The performance of these organizations relies primarily on that of their workforce, apart from other factors such as infrastructure and environment. At the national level, the pressure on managing the human resources (HR) issues keeps increasing as the population grows. Recruiting and retaining valuable personnel are the primary issues of the labor market. The most critical recruitment problem is matching the appropriate candidates to the right job. With several thousands of candidates and hundreds of jobs, it is imperative to automate the matching process as it is a herculean task to make the matches manually. The HR industry is also one of the largest domains, where the entire system is predominantly text driven in the form of résumés and Job postings. Each candidate and every employer communicate their requirements in plain text mostly in free-running format of respective styles. In Arab countries, these texts are written mostly in Arabic or English. A cross-lingual job search engine would be useful for jobseekers searching posts and for employers retrieving candidate profiles. Suitable Information Extraction and Natural Language Processing (NLP) techniques need to be applied. The proposed research investigates possible avenues for tackling the relevant problems associated in this regard and normalize the employability pathways in the Sultanate of Oman. As a result, this research work is beneficial to citizens and migrant workers in Oman. Furthermore, the results could be extrapolated to other countries.
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation

Recent studies have shown the effectiveness of retrieval augmentation in many generative NLP tasks.These retrieval-augmented methods allow models to explicitly acquire prior external knowledge in a non-parametric manner and regard the retrieved reference instances as cues to augment text generation.These methods use similarity-based retrieval, which is based on a simple hypothesis: the more the retrieved demonstration resembles the original input, the more likely the demonstration label resembles the input label.However, due to the complexity of event labels and sparsity of event arguments, this hypothesis does not always hold in document-level EAE.This raises an interesting question: How do we design the retrieval strategy for document-level EAE?We investigate various retrieval settings from the input and label distribution views in this paper.We further augment document-level EAE with pseudo demonstrations sampled from event semantic regions that can cover adequate alternatives in the same context and event schema.Through extensive experiments on RAMS and WikiEvents, we demonstrate the validity of our newly introduced retrieval-augmented methods and analyze why they work.
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation

Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can contain errors from both summarization and translation processes.To address this issue, we propose ConvSumX, a cross-lingual conversation summarization benchmark, through a new annotation schema that explicitly considers source input context.ConvSumX consists of 2 sub-tasks under different real-world scenarios, with each covering 3 language directions.We conduct thorough analysis on ConvSumX and 3 widely-used manually annotated CLS corpora and empirically find that ConvSumX is more faithful towards input text.Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.Experimental results show that 2-Step method surpasses strong baselines on ConvSumX under both automatic and human evaluation.Analysis shows that both source input text and summary are crucial for modeling cross-lingual summaries.
On strike against AI

The threat of artificial intelligence-authored content hangs over many creative industries. Actors and writers need to push back against automation, says Annalee Newitz
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset

Research in massively multilingual image captioning has been severely hampered by a lack of high-quality evaluation datasets.In this paper we present the Crossmodal-3600 dataset (XM3600 in short), a geographically-diverse set of 3600 images annotated with humangenerated reference captions in 36 languages.The images were selected from across the world, covering regions where the 36 languages are spoken, and annotated with captions that achieve consistency in terms of style across all languages, while avoiding annotation artifacts due to direct translation.We apply this benchmark to model selection for massively multilingual image captioning models, and show strong correlation results with human evaluations when using XM3600 as golden references for automatic metrics.
EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments

EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. The goal is to jointly determine the bitwidths employed for local FL model quantization and the set of devices participating in FL training at each iteration. We pose this as an optimization problem that aims to minimize the training loss of quantized FL under a per-iteration device sampling budget and delay requirement. However, the formulated problem is difficult to solve without (i) a concrete understanding of how quantization impacts global ML performance and (ii) the ability of the server to construct estimates of this process efficiently. To address the first challenge, we analytically characterize how limited wireless resources and induced quantization errors affect the performance of the proposed FL method. Our results quantify how the improvement of FL training loss between two consecutive iterations depends on the device selection and quantization scheme as well as on several parameters inherent to the model being learned. Then, to address the second challenge, we show that the FL training process can be described as a Markov decision process (MDP) and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Simulation results show that the proposed FL algorithm can reduce the convergence time by 29% and 63% compared to a model free RL method and the standard FL method, respectively.
TOME: A Two-stage Approach for Model-based Retrieval

Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters.This design employs a sequence-to-sequence paradigm to generate document identifiers, which enables the complete capture of the relevance between queries and documents and simplifies the classic indexretrieval-rerank pipeline.Despite its attractive qualities, there remain several major challenges in model-based retrieval, including the discrepancy between pre-training and fine-tuning, and the discrepancy between training and inference.To deal with the above challenges, we propose a novel two-stage model-based retrieval approach called TOME, which makes two major technical contributions, including the utilization of tokenized URLs as identifiers and the design of a two-stage generation architecture.We also propose a number of training strategies to deal with the training difficulty as the corpus size increases.Extensive experiments and analysis on MS MARCO and Natural Questions demonstrate the effectiveness of our proposed approach, and we investigate the scaling laws of TOME by examining various influencing factors.
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge

Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes.Prior work has studied targeted updates to LMs, injecting individual facts and evaluating whether the model learns these facts while not changing predictions on other contexts.We take a step forward and study LMs' abilities to make inferences based on injected facts (or propagate those facts): for example, after learning that something is a TV show, does an LM predict that you can watch it?We study this with two clozestyle tasks: an existing dataset of real-world sentences about novel entities (ECBD) as well as a new controlled benchmark with manually designed templates requiring varying levels of inference about injected knowledge.Surprisingly, we find that existing methods for updating knowledge (gradient-based fine-tuning and modifications of this approach) show little propagation of injected knowledge.These methods improve performance on cloze instances only when there is lexical overlap between injected facts and target inferences.Yet, prepending entity definitions in an LM's context improves performance across all settings, suggesting that there is substantial headroom for parameterupdating approaches for knowledge injection.
Diable: Efficient Dialogue State Tracking as Operations on Tables

Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.This approach is inefficient, especially when the number of slots is large and the conversation is long.We propose Diable, a new task formalisation that simplifies the design and implementation of efficient DST systems and allows one to easily plug and play large language models.We represent the dialogue state as a table and formalise DST as a table manipulation task.At each turn, the system updates the previous state by generating table operations based on the dialogue context.Extensive experimentation on the MultiWoz datasets demonstrates that Diable (i) outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient than current state-of-the-art methods while retaining competitive Joint Goal Accuracy, and (iii) is robust to noisy data annotations due to the table operations approach.efficient-dialogue-state-tracking-bysequential-information-processing
Improving Radiology Summarization with Radiograph and Anatomy Prompts

The impression is crucial for the referring physicians to grasp key information since it is concluded from the findings and reasoning of radiologists.To alleviate the workload of radiologists and reduce repetitive human labor in impression writing, many researchers have focused on automatic impression generation.However, recent works on this task mainly summarize the corresponding findings and pay less attention to the radiology images.In clinical, radiographs can provide more detailed valuable observations to enhance radiologists' impression writing, especially for complicated cases.Besides, each sentence in findings usually focuses on single anatomy, such that they only need to be matched to corresponding anatomical regions instead of the whole image, which is beneficial for textual and visual features alignment.Therefore, we propose a novel anatomyenhanced multimodal model to improve impression generation.In detail, we first construct a set of rules to extract anatomies and put these prompts into each sentence to highlight anatomy characteristics.Then, two separate encoders are applied to extract features from the radiograph and findings.Afterward, we apply a contrastive learning module to align these two representations at the overall level and use a co-attention to fuse them at the sentence level with the help of anatomy-enhanced sentence representation.The experimental results on two benchmark datasets confirm the effectiveness of the proposed method, which achieves state-of-the-art results.
CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation

End-to-end speech translation (ST) is the task of translating speech signals in the source language into text in the target language.As a cross-modal task, end-to-end ST is difficult to train with limited data.Existing methods often try to transfer knowledge from machine translation (MT), but their performances are restricted by the modality gap between speech and text.In this paper, we propose Cross-modal Mixup via Optimal Transport (CMOT) to overcome the modality gap.We find the alignment between speech and text sequences via optimal transport and then mix up the sequences from different modalities at a token level using the alignment.Experiments on the MuST-C ST benchmark demonstrate that CMOT achieves an average BLEU of 30.0 in 8 translation directions, outperforming previous methods.Further analysis shows CMOT can adaptively find the alignment between modalities, which helps alleviate the modality gap between speech and text.
Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection

We study the problem of cross-lingual transfer learning for event detection (ED) where models trained on a source language are expected to perform well on data for a new target language.Among a few recent works for this problem, the main approaches involve representation matching (e.g., adversarial training) that aims to eliminate language-specific features from the representations to achieve the language-invariant representations.However, due to the mix of language-specific features with event-discriminative context, representation matching methods might also remove important features for event prediction, thus hindering the performance for ED.To address this issue, we introduce a novel approach for cross-lingual ED where representations are augmented with additional context (i.e., not eliminating) to bridge the gap between languages while enriching the contextual information to facilitate ED.At the core of our method involves a retrieval model that retrieves relevant sentences in the target language for an input sentence to compute augmentation representations.Experiments on three languages demonstrate the state-of-the-art performance of our model for cross-lingual ED.
BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages.To extend the benefits of BLOOM to other languages without incurring prohibitively large costs, it is desirable to adapt BLOOM to new languages not seen during pretraining.In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting.We find language adaptation to be effective at improving zero-shot performance in new languages.Surprisingly, we find that adapter-based finetuning is more effective than continued pretraining for large models.In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system.It is primarily determined by the size of the language adaptation data.We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot.We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language.We conclude that with sufficient training data language adaptation can generalize well to diverse languages.Our code is available at https: //github.com/bigscience-workshop/multilingual-modeling.
Large Language Models with Controllable Working Memory

Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), partly owing to the massive amounts of world knowledge they memorize during pretraining.While many downstream applications provide the model with an informational context to aid its underlying task, how the model's world knowledge interacts with the factual information presented in the context remains under explored.As a desirable behavior, an LLM should give precedence to the context whenever it contains taskrelevant information that conflicts with the model's memorized knowledge.This enables model predictions to be grounded in the context, which then facilitates updating specific model predictions without frequently retraining the model.By contrast, when the context is irrelevant to the task, the model should ignore it and fall back on its internal knowledge.In this paper, we undertake a first joint study of the aforementioned two properties, namely controllability and robustness, in the context of LLMs.We demonstrate that state-of-theart T5 and PaLM models (both pretrained and finetuned) could exhibit low controllability and robustness that does not improve with increasing the model size.As a solution, we propose a simple yet effective method knowledge aware finetuning (KAFT) -to strengthen both controllability and robustness by injecting counterfactual and irrelevant contexts to standard supervised datasets.Our comprehensive evaluation showcases the utility of KAFT across model architectures and sizes.
Nonparametric Masked Language Modeling

Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases.We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus.NPM fills in the [MASK] solely from retrieving a token from a text corpus.We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval.Zero-shot evaluation on 16 tasks including classification, fact probing and question answering demonstrates that NPM outperforms significantly larger parametric models, either with or without a retrieve-and-generate approach.It is particularly better at dealing with rare patterns (word senses or facts) and predicting rare or nearly unseen words (e.g., non-Latin script).We release the model and code at github.com/facebookresearch/NPM.
Rethinking the Word-level Quality Estimation for Machine Translation from Human Judgement

Word-level Quality Estimation (QE) of Machine Translation (MT) aims to detect potential translation errors in the translated sentence without reference.Typically, conventional works on word-level QE are usually designed to predict the quality of translated words in terms of the post-editing effort, where the word labels in the dataset， i.e., OK or BAD, are automatically generated by comparing words between MT sentences and the post-edited sentences through a Translation Error Rate (TER) toolkit.While the post-editing effort can be used to measure the translation quality to some extent, we find it usually conflicts with human judgment on whether the word is well or poorly translated.To investigate this conflict, we first create a golden benchmark dataset, namely HJQE (Human Judgement on Quality Estimation), where the source and MT sentences are identical to the original TER-based dataset and the expert translators directly annotate the poorly translated words on their judgments.Based on our analysis, we further propose two tag-correcting strategies which can make the TER-based artificial QE corpus closer to HJQE.We conduct substantial experiments based on the publicly available WMT En-De and En-Zh corpora.The results not only show our proposed dataset is more consistent with human judgment but also confirm the effectiveness of the proposed tag-correcting strategies. 1 Alignment generated by TER toolkit