Target Sentence Research Articles

Text-based and memory-based metrics of cognitive coupling.

The present study was an investigation of the relation between cognitive coupling, a correlation between text difficulty and reading time, and other measures of mind wandering during reading. To measure cognitive coupling, we manipulated the text difficulty of individual sentences. Because mind wandering may shift attention away from the text, we predicted a cognitive coupling interaction, that is, that the effect of difficulty on processing time should be less when readers are off task. We also manipulated the consistency of a target sentence's content with a prior information. Analogous to the text-based cognitive coupling, we predicted an interaction of consistency with task focus: The impact of this consistency should be less noticeable when readers are off task. The results demonstrated the predicted text-based cognitive-coupling effect: There was less of an effect of text difficulty when readers reported being off task. However, there was no such interaction between consistency and task focus. We conclude that the consistency effect may depend on the relatively automatic activation of prior information rather than requiring consciously retrieving related information from memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

What can the taxonomy of predicative possession in Malwai Punjabi tell us?

Abstract The study aims to examine the syntactic and semantic behaviors of predicative possession (i.e., have-possessive constructions) in Malwai Punjabi, an underdocumented dialect within the Indo-Aryan language family. Data were collected from longitudinal online interviews with native speakers as consultants, with audio recordings for transcribed target sentences. The results revealed that all the alienable possession, either permanent/temporary or abstract/concrete, could be marked by the postposition koḷ ‘near/with’, whereas inalienable possession, such as whole-part relation and kinship, could not be encoded using koḷ. The prototypicality model and schema-based metaphors explained why koḷ was widely used to express alienable possession in Malwai Punjabi. The analysis of companion and proximity schemata also justified the extended semantics of predicative possession, suggesting a metaphorical mapping of accompaniment and location onto possession. From a typological angle, the case study can not only provide further evidence for the existence of split possession but also contribute to a cognitive understanding of predicative possession in relation to other languages.

Knowledge Graph Guided Neural Machine Translation with Dynamic Reinforce-selected Triples

Previous methods incorporating knowledge graphs (KGs) into neural machine translation (NMT) adopt a static knowledge utilization strategy, that introduces many useless knowledge triples and makes the useful triples difficult be utilized by NMT. To address this problem, we propose a KG guided NMT model with dynamic reinforce-selected triples. The proposed methods could dynamically select the different useful knowledge triples for different source sentences. Specifically, the proposed model contains two components: 1) knowledge selector, that dynamically selects useful knowledge triples for a source sentence, and 2) knowledge guided NMT (KgNMT), that utilizes the selected triples to guide the translation of NMT. Meanwhile, to overcome the non-differentiable problem and guide the training procedure, we propose a policy gradient strategy to encourage the model to select useful triples and improve the generation probability of gold target sentence. Various experimental results show that the proposed method can significantly outperform the baseline models in both translation quality and handling the entities.

Benefits of Cochlear Implantation for Older Adults With Asymmetric Hearing Loss.

FDA-approved indications for cochlear implantation include patients with severe-to-profound unilateral hearing loss (UHL) or asymmetric hearing loss (AHL); however, these indications are not covered for Medicare beneficiaries. We assessed the outcomes of cochlear implant (CI) use for older adults with UHL or AHL. Eighteen older adults (≥65 years of age at surgery) with UHL/AHL participated in a prospective, longitudinal investigation evaluating outcomes of CI use. Speech recognition for the affected ear was evaluated with consonant-nucleus-consonant (CNC) words. Spatial hearing was assessed with measures of sound source localization and sentence recognition in noise. The target sentence was presented from the front and the masker was either co-located with the target (SoNo), presented toward the affected ear (SoNci) or contralateral ear (SoNcontra). Perceived benefit was assessed with the Speech, Spatial, and Qualities of Hearing scale (SSQ) and the Tinnitus Handicap Inventory (THI). Participants experienced significant improvements with CI use for CNC words (mean [SD]; preop: 8% [10%], 1 yr: 51% [22%], 5 yr: 50% [19%]), masked sentence recognition (SoNcontra preop: 5% [6%], 1 yr: 22% [15%], 5 yr: 41% [14%]), and localization (preop: 76° [18°], 1 yr: 40° [11°], 5 yr: 41° [14°]),and reported significant improvements in hearing abilities (SSQ Spatial Hearing preop: 3 [1], 1 yr: 6 [2], 5 yr: 6 [2]) and tinnitus severity (THI preop: 16 [18], 1 yr: 4 [14], 5 yr: 6 [12]). Older adults with UHL/AHL experience significant improvements in speech recognition, spatial hearing, and subjective perceptions (e.g., hearing abilities and tinnitus severity) with a CI as compared to pre-operative abilities. IV Laryngoscope, 2024.

Why second-language speakers sometimes, but not always, derive scalar inferences like first-language speakers: Effects of task demands

ABSTRACT Many studies report that the computation of scalar inferences, such as the inference from ‘some’ to ‘not all’, is cognitively costly. However, a number of studies on scalar inferences in L2 suggest the opposite conclusion, reporting that, even though processing L2-input is often thought to be cognitively demanding, people are at least as likely to compute scalar inferences in L2 as L1. Here, we propose that cognitive difficulties with computing scalar inferences in L2 may be masked by the use of experimental tasks that encourage participants to take their time to process target sentences. Thus, we found that, in a self-paced pen-and-paper experiment, participants did not significantly differ in the propensity with which they computed scalar inferences in L1 and L2. However, in a computerized experiment in which sentences were flashed on screen, low-proficiency L2 speakers were significantly less likely to compute scalar inferences in L2 than L1. These results underscore the importance of analyzing task demands in research on pragmatics and multilingualism.

Intelligent Emergency Assisted Decision-Making Method Based on Standard Digitalization: Hazardous Chemical Accidents in Industrial Parks

Contemporary society is confronted with multifaceted challenges, and the intricate interplay of interconnected factors significantly complicates emergency response efforts. Current practices rely on quick decisions by domain experts; however, the limitations of individual expertise and the urgency of crises hinder both precision and standardization. To address these issues, we propose a novel approach: an intelligent method for emergency decision making grounded in a standardized digital knowledge graph. First, our study examined the underlying theory of standardized digital transformation and event-chain evolution. This led to the construction of a knowledge graph encompassing standard emergency knowledge, as well as supplementary derivative data pertinent to event response. Second, through the application of semantic analysis and intention recognition of the decision target, coherent and interpretable query sentences for the decision system were crafted. These query sentences then served as a conduit for retrieving standard emergency knowledge relevant to the current emergency situation, as well as potential secondary disasters. The overarching goal is to provide emergency decision makers with effective support mechanisms that are both well informed and tailored to the specific demands of each situation.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation.

Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly adopt a supervised approach and rely heavily on coupled data-to-text pairs. However, for many targeted scenarios and for non-English languages, sufficient quantities of labeled data are often not available. As a result, it is necessary to collect and label data-text pairs for training, which is both costly and time-consuming. To relax the dependency on labeled data of downstream tasks, we propose an intuitive and effective zero-shot learning framework, ZeroNLG, which can deal with multiple NLG tasks, including image-to-text (image captioning), video-to-text (video captioning), and text-to-text (neural machine translation), across English, Chinese, German, and French within a unified framework. ZeroNLG does not require any labeled downstream pairs for training. During training, ZeroNLG (i) projects different domains (across modalities and languages) to corresponding coordinates in a shared common latent space; (ii) bridges different domains by aligning their corresponding coordinates in this space; and (iii) builds an unsupervised multilingual auto-encoder to learn to generate text by reconstructing the input text given its coordinate in shared latent space. Consequently, during inference, based on the data-to-text pipeline, ZeroNLG can generate target sentences across different languages given the coordinate of input data in the common space. Within this unified framework, given visual (imaging or video) data as input, ZeroNLG can perform zero-shot visual captioning; given textual sentences as input, ZeroNLG can perform zero-shot machine translation. We present the results of extensive experiments on twelve NLG tasks, showing that, without using any labeled downstream pairs for training, ZeroNLG generates high-quality and "believable" outputs and significantly outperforms existing zero-shot methods.

Context, transparency and culture in motor resonance phenomena: Causal evidence of the motor cortex

A connection between language and movement information in metaphorical and literal expressions activates the motor system. Despite numerous studies exploring distinctions between idioms and metaphors, a notable research gap remains in the specific effect of idioms with different transparency levels concerning motor resonance. Our primary focus was analysing the functional role of the primary motor cortex (M1) in processing hand motor verbs both in literal expressions and in two idiomatic contexts, i.e., opaque and transparent idioms. Additionally, we explored a potential language and cultural effect by comparing Turkish and Spanish speakers. An overt priming task with self-paced reading was used to judge the relatedness of a primer and a target sentence. We implemented a repetitive transcranial magnetic stimulation (TMS) protocol using continuous theta-burst stimulation (cTBS) compared to sham stimulation over the M1 in both Turkish and Spanish native speakers prior to the experimental task. Our findings reveal that the performance of Turkish and Spanish participants in processing hand motor actions was facilitated after the application of cTBS over the left M1. Moreover, brain stimulation specifically facilitated the processing of only transparent—but not opaque—idioms in both Spanish and Turkish participants. Our study reports distinct motor resonance results between different types of idioms with a parallel cross-cultural effect.

When prospective information conflicts with current information

ABSTRACT We examined the impact of prospective information on the processing of information occurring within the present timeline of narrative stories. Participants read target sentences that were consistent with events occurring within a protagonist’s present timeline but inconsistent with events in the protagonist’s future. When prospective information was readily available and/or received strong contextual support, processing difficulties were immediate, (i.e. they occurred on the target sentence). When the prospective information was backgrounded and/or not supported with additional contextual information, processing disruptions were delayed until the spillover sentence. Results are discussed within memory-based theories, specifically the RI-Val model.

The Effects of Light Level and Signal-to-Noise Ratio on the Task-Evoked Pupil Response in a Speech-in-Noise Task.

There is increasing interest in the measurement of cognitive effort during listening tasks, for both research and clinical purposes. Quantification of task-evoked pupil responses (TEPRs) is a psychophysiological method that can be used to study cognitive effort. However, light level during cognitively demanding listening tasks may affect TEPRs, complicating interpretation of listening-related changes. The objective of this study was to examine the effects of light level on TEPRs during effortful listening across a range of signal-to-noise ratios (SNRs). Thirty-six adults without hearing loss were asked to repeat target sentences presented in background babble noise while their pupil diameter was recorded. Light level and SNRs were manipulated in a 4 × 4 repeated-measures design. Repeated-measures analyses of variance were used to measure the effects. Peak and mean dilation were typically larger in more adverse SNR conditions (except for SNR -6 dB) and smaller in higher light levels. Differences in mean and peak dilation between SNR conditions were larger in dim light than in brighter light. Brighter light conditions make TEPRs less sensitive to variations in listening effort across levels of SNR. Therefore, light level must be considered and reported in detail to ensure sensitivity of TEPRs and for comparisons of findings across different studies. It is recommended that TEPR testing be conducted in relatively low light conditions, considering both background illumination and screen luminance. https://doi.org/10.23641/asha.25676538.

Speech Prosody Serves Temporal Prediction of Language via Contextual Entrainment.

Temporal prediction assists language comprehension. In a series of recent behavioral studies, we have shown that listeners specifically employ rhythmic modulations of prosody to estimate the duration of upcoming sentences, thereby speeding up comprehension. In the current human magnetoencephalography (MEG) study on participants of either sex, we show that the human brain achieves this function through a mechanism termed entrainment. Through entrainment, electrophysiological brain activity maintains and continues contextual rhythms beyond their offset. Our experiment combined exposure to repetitive prosodic contours with the subsequent presentation of visual sentences that either matched or mismatched the duration of the preceding contour. During exposure to prosodic contours, we observed MEG coherence with the contours, which was source-localized to right-hemispheric auditory areas. During the processing of the visual targets, activity at the frequency of the preceding contour was still detectable in the MEG; yet sources shifted to the (left) frontal cortex, in line with a functional inheritance of the rhythmic acoustic context for prediction. Strikingly, when the target sentence was shorter than expected from the preceding contour, an omission response appeared in the evoked potential record. We conclude that prosodic entrainment is a functional mechanism of temporal prediction in language comprehension. In general, acoustic rhythms appear to endow language for employing the brain's electrophysiological mechanisms of temporal prediction.

Knowledge revision processes during reading: How pictures influence the activation of outdated information.

Outdated information (i.e., information that is not or no longer accurate) continues to be automatically activated during reading and can hinder learning processes. Thus, it is important to understand which factors influence the activation of outdated information and, therefore, knowledge revision processes. In three online experiments, we investigated how illustrating updated or outdated information via pictures influences the activation of outdated information. In Experiments 1 (N = 421) and 2 (N = 422), we varied whether participants read texts containing outdated information that was later updated (outdated text) or texts containing only updated information (consistent text). In addition, the updated information was or was not illustrated by a picture. In Experiment 3 (N = 441), participants read outdated texts, and we varied whether the outdated, the updated, or no information was illustrated. In all experiments, we measured reading times for a target sentence referring to the updated information and the sentence following the target sentence. Results showed that target sentences' reading times were faster for illustrated than for non-illustrated texts (Experiments 1 and 2). Moreover, reading times were longer when the outdated information was illustrated than when the updated information was illustrated (Experiment 3). These results suggest that pictures overall facilitate cognitive processes during reading, but their content matters: Pictures showing the updated information had a greater impact on reading times than pictures showing the outdated information. The results extend existing theories on knowledge revision but also reading comprehension, by demonstrating how pictures might influence cognitive processes during reading.

Changes in the Syntactic Structure in English-Arabic Simultaneous Interpreting

Simultaneous interpreting represents a challenge for interpreters working between typologically distinct languages, like the English-Arabic language pair. The present study is conducted to explore transfers in passive voice verbs between English as the source language and Arabic as the target language in a corpus of an interpreted English speech and its corresponding Arabic counterpart. A mixed qualitative and quantitative approach is adopted in the present study to identify recurrences of passive voice verbs and to explore the ways of rendering them into Arabic. Translation shifts are identified according to Catford’s (1965) approach. The findings of the present study reveal that nearly half of the passive voice verbs found in the source text are rendered using active voice verbs in the target text. The findings also reveal that there are four other ways of rendering passive voice verbs, including using a noun phrase, omitting a part of a sentence or a sentence as a whole, using a noun, or maintaining the passive voice form of the source verb in the target sentence. The present study contributes to promoting practices that enrich the field of English-Arabic simultaneous interpreting. It also contributes to enriching the research domain with its findings that hold special significance as the passive voice verbs constitute a divergence point between the two languages.

Marathi to Indian Sign Language Machine Translation

Machine translation has been a prominent field of research, contributing significantly to human life enhancement. Sign language machine translation, a subfield, focuses on translating spoken language content into sign language and vice versa, thereby facilitating communication between the normal hearing and hard-of-hearing communities, promoting inclusivity. This study presents the development of a ‘sign language machine translation system’ converting simple Marathi sentences into Indian Sign Language (ISL) glosses and animation. Given the low-resource nature of both languages, a phrase-level rule-based approach was employed for the translation. Initial encoding of translation rules relied on basic linguistic knowledge of Marathi and ISL, with subsequent incorporation of rules to address 'simultaneous morphological' features in ISL. These rules were applied during the ‘generation phase’ of translation to dynamically adjust phonological sign parameters, resulting in improved target sentence fluency. The paper provides a detailed description of the system architecture, translation rules, and comprehensive experimentation. Rigorous evaluation efforts were undertaken, encompassing various linguistic features, and the findings are discussed herein. The web-based version of the system serves as an interpreter for brief communications and can support the teaching and learning of sign language and its grammar in schools for hard-of-hearing students.

The role of long-term target and masker talker familiarity in children's speech-in-speech recognition.

This study investigated the influence of long-term talker familiarity on speech-in-speech recognition in school-age children, with a specific emphasis on the role of familiarity with the mother's voice as either the target or masker speech. Open-set sentence recognition was measured adaptively in a two-talker masker. Target and masker sentences were recorded by the adult mothers of the child participants. Each child heard sentences spoken by three adult female voices during testing; their own mother's voice (familiar voice) and two unfamiliar adult female voices. Twenty-four school age children (8-13 years) with normal hearing. When the target speech was spoken by a familiar talker (the mother), speech recognition was significantly better compared to when the target was unfamiliar. When the masker was spoken by the familiar talker, there was no difference in performance relative to the unfamiliar masker condition. Across all conditions, younger children required a more favorable signal-to-noise ratio than older children. Implicit long-term familiarity with a talker consistently improves children's speech-in-speech recognition across the age range tested, specifically when the target talker is familiar. However, performance remains unaffected by masker talker familiarity. Additionally, while target familiarity is advantageous, it does not entirely eliminate children's increased susceptibility to competing speech.

Objective and Subjective Measures of Spatial Hearing in Unilateral Cochlear Implant Users with Bilateral Profound Hearing Loss

Purpose: The ability to benefit from spatial separation between target and masker signals is important in multi-sound source listening environments. The goal of this study was to measure the spatial release from masking (SRM) in unilateral cochlear implant (CI) users with bilateral profound hearing loss. We also determined the relationships between the SRMs and the self-reported spatial hearing abilities.Methods: Fourteen unilateral CI users with bilateral profound hearing loss participated in this study. The target sentence was always presented to the front of the listener, and the nonfluctuating speech-shaped noise (SSN) or fluctuating speech noise was either co-located with the target (speech at 0°, noise at 0°, S0N0) or spatially separated at ± 90°. The SRM was quantified as the difference between speech recognition thresholds (SRTs) in the co-located and spatially separated conditions. The self-reported spatial hearing abilities were also measured using validated subjective questionnaires.Results: Overall, the SRTs were lower (better) with SSN than with fluctuating speech noise. When the noise was presented to the non-CI ear (speech at 0°, noise at non-CI ear, S0Nnonci), speech-in-noise recognition was the greatest due to head shadow or better-ear listening effect, resulting in the SRMs of approximately 5~6 dB regardless of noise type. When the noise was given to the CI ear (speech at 0°, noise at CI ear, S0Nci), some individuals exhibited positive SRMs (3~8 dB), while others showed negative SRMs, leading to little SRMs overall. When the SSN was given, subjects with less SRMs (less spatial separation benefits on the objective test) reported greater subjective spatial hearing difficulties.Conclusion: The spatial hearing of unilateral CI users varied by the position of the sound source. Listeners' spatial hearing abilities, which are unpredictable from clinical routine tests, need to be assessed by either objective or subjective measures.

Development and Validation of the Spatial Separation Sentence Test in Kannada.

This study aimed to develop and validate a modified version of the Speech in Noise Sentence Test in Kannada, which would be appropriate for testing the speech comprehension ability of children aged 8-12 years. A total of 120 sentences were chosen from 200 familiar sentences and split into four lists. Continuous discourse was used as a competition or distractor. Using MATLAB, the target stimulus was presented at 0-degree azimuth while the distractor's location varied (+90° and -90° azimuth). The test was programmed to dynamically adjust the signal-to-noise ratio (SNR) based on participants' responses. After initial validation, a pilot study was conducted with 60 typically hearing children aged 8 to 12 years. The SNR50 scores significantly improved when the distractor and target sentences were spatially separated across all groups. Age had a significant influence on the spatial separation scores. The test-retest reliability was excellent. The developed stimuli effectively measured spatial separation, and the normative and psychometric analyses demonstrated reliable outcomes.

Novel algorithm machine translation for language translation tool

AbstractFuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

Planning for the future and reacting to the present: Proactive and reactive F0 adjustments in speech

Previous studies have examined whether speakers initiate longer utterances with higher F0. Evidence for such effects is mixed and is mostly based on point estimates of F0 at the beginning of the utterance. Moreover, it is unknown whether utterance length can influence F0 control solely at utterance onset or also during the utterance. We conducted a sentence production task to investigate how control of pitch register – F0 ceiling, floor, and span – is influenced by utterance length. Specifically, we test whether speakers adjust register both in relation to an initially planned utterance length – proactive F0 control – and in response to changes in utterance length that occur after response onset – reactive F0 control. Target sentences in the experiment had one, two, or three subject noun phrases, which were cued with visual stimuli. An experimental manipulation was tested in which some visual stimuli were delayed until after participants initiated the utterance. Evidence for both proactive and reactive control of register was observed. Participants adopted a higher register ceiling and broader span in longer utterances. Furthermore, they decreased the amount of ceiling compression upon encountering delayed stimuli. The findings suggest the presence of a mechanism in which speakers continuously estimate the remaining length of the utterance and use that information to adjust pitch register.

MCL-NER: Cross-Lingual Named Entity Recognition via Multi-View Contrastive Learning

Cross-lingual named entity recognition (CrossNER) faces challenges stemming from uneven performance due to the scarcity of multilingual corpora, especially for non-English data. While prior efforts mainly focus on data-driven transfer methods, a significant aspect that has not been fully explored is aligning both semantic and token-level representations across diverse languages. In this paper, we propose Multi-view Contrastive Learning for Cross-lingual Named Entity Recognition (MCL-NER). Specifically, we reframe the CrossNER task into a problem of recognizing relationships between pairs of tokens. This approach taps into the inherent contextual nuances of token-to-token connections within entities, allowing us to align representations across different languages. A multi-view contrastive learning framework is introduced to encompass semantic contrasts between source, codeswitched, and target sentences, as well as contrasts among token-to-token relations. By enforcing agreement within both semantic and relational spaces, we minimize the gap between source sentences and their counterparts of both codeswitched and target sentences. This alignment extends to the relationships between diverse tokens, enhancing the projection of entities across languages. We further augment CrossNER by combining self-training with labeled source data and unlabeled target data. Our experiments on the XTREME benchmark, spanning 40 languages, demonstrate the superiority of MCL-NER over prior data-driven and model-based approaches. It achieves a substantial increase of nearly +2.0 F1 scores across a broad spectrum and establishes itself as the new state-of-the-art performer.