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1. A Dataset of Word-Complexity Judgements from Deaf and Hard-of-Hearing Adults for Text Simplification

   https://doi.org/10.18653/v1/2022.tsar-1.11  

   Alonzo, Oliver; Lee, Sooyeon; Maddela, Mounica; Xu, Wei; Huenerfauth, Matt (December 2022, Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022))

   Research has explored the use of automatic text simplification (ATS), which consists of techniques to make text simpler to read, to provide reading assistance to Deaf and Hard-of-hearing (DHH) adults with various literacy levels. Prior work in this area has identified interest in and benefits from ATS-based reading assistance tools. However, no prior work on ATS has gathered judgements from DHH adults as to what constitutes complex text. Thus, following approaches in prior NLP work, this paper contributes new word-complexity judgements from 11 DHH adults on a dataset of 15,000 English words that had been previously annotated by L2 speakers, which we also augmented to include automatic annotations of linguistic characteristics of the words. Additionally, we conduct a supplementary analysis of the interaction effect between the linguistic characteristics of the words and the groups of annotators. This analysis highlights the importance of collecting judgements from DHH adults for training ATS systems, as it revealed statistically significant interaction effects for nearly all of the linguistic characteristics of the words. 

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2. Remotely Co-Designing Features for Communication Applications using Automatic Captioning with Deaf and Hearing Pairs

   https://doi.org/10.1145/3491102.3501843  

   Seita, Matthew; Lee, Sooyeon; Andrew, Sarah; Shinohara, Kristen; Huenerfauth, Matt (April 2022, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22))

   Deaf and Hard-of-Hearing (DHH) users face accessibility challenges during in-person and remote meetings. While emerging use of applications incorporating automatic speech recognition (ASR) is promising, more user-interface and user-experience research is needed. While co-design methods could elucidate designs for such applications, COVID-19 has interrupted in-person research. This study describes a novel methodology for conducting online co-design workshops with 18 DHH and hearing participant pairs to investigate ASR-supported mobile and videoconferencing technologies along two design dimensions: Correcting errors in ASR output and implementing notification systems for influencing speaker behaviors. Our methodological findings include an analysis of communication modalities and strategies participants used, use of an online collaborative whiteboarding tool, and how participants reconciled differences in ideas. Finally, we present guidelines for researchers interested in online DHH co-design methodologies, enabling greater geographically diversity among study participants even beyond the current pandemic. 

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3. BiTe-GCN: A New GCN Architecture via Bidirectional Convolution of Topology and Features on Text-Rich Networks

   https://doi.org/10.1145/3437963.3441774  

   Jin, Di; Song, Xiangchen; Yu, Zhizhi; Liu, Ziyang; Zhang, Heling; Cheng, Zhaomeng; Han, Jiawei (March 2021, WSDM'21, The Fourteenth ACM International Conference on Web Search and Data Mining, March 2021)

   Liane Lewin-Eytan, David Carmel (Ed.)

   Graph convolutional networks (GCNs), aiming to obtain node embeddings by integrating high-order neighborhood information through stacked graph convolution layers, have demonstrated great power in many network analysis tasks such as node classification and link prediction. However, a fundamental weakness of GCNs, that is, topological limitations, including over-smoothing and local homophily of topology, limits their ability to represent networks. Existing studies for solving these topological limitations typically focus only on the convolution of features on network topology, which inevitably relies heavily on network structures. Moreover, most networks are text-rich, so it is important to integrate not only document-level information, but also the local text information which is particularly significant while often ignored by the existing methods. To solve these limitations, we propose BiTe-GCN, a novel GCN architecture modeling via bidirectional convolution of topology and features on text-rich networks. Specifically, we first transform the original text-rich network into an augmented bi-typed heterogeneous network, capturing both the global document-level information and the local text-sequence information from texts.We then introduce discriminative convolution mechanisms, which performs convolution on this augmented bi-typed network, realizing the convolutions of topology and features altogether in the same system, and learning different contributions of these two parts (i.e., network part and text part), automatically for the given learning objectives. Extensive experiments on text-rich networks demonstrate that our new architecture outperforms the state-of-the-arts by a breakout improvement. Moreover, this architecture can also be applied to several e-commerce search scenes such as JD searching, and experiments on JD dataset show the superiority of the proposed architecture over the baseline methods. 

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4. Automatic Text Simplification Tools for Deaf and Hard of Hearing Adults: Benefits of Lexical Simplification and Providing Users with Autonomy

   https://doi.org/10.1145/3313831.3376563  

   Alonzo, Oliver; Seita, Matthew; Glasser, Abraham; Huenerfauth, Matt (April 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Automatic Text Simplification (ATS), which replaces text with simpler equivalents, is rapidly improving. While some research has examined ATS reading-assistance tools, little has examined preferences of adults who are deaf or hard-of-hearing (DHH), and none empirically evaluated lexical simplification technology (replacement of individual words) with these users. Prior research has revealed that U.S. DHH adults have lower reading literacy on average than their hearing peers, with unique characteristics to their literacy profile. We investigate whether DHH adults perceive a benefit from lexical simplification applied automatically or when users are provided with greater autonomy, with on-demand control and visibility as to which words are replaced. Formative interviews guided the design of an experimental study, in which DHH participants read English texts in their original form and with lexical simplification applied automatically or on-demand. Participants indicated that they perceived a benefit form lexical simplification, and they preferred a system with on-demand simplification. 

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5. Effects of kratom alkaloids on mesolimbic dopamine release

   https://doi.org/10.1016/j.neulet.2025.138153  

   Manus, James P; Crenshaw, Rebecca C; Ringer, Lindsay C; Towers, Shelby A; Paige, Nick B; Leon, Francisco; McCurdy, Christopher R; Lester, Deranda B (February 2025, Neuroscience Letters)

   Kratom is derived from the leaves of a plant (Mitragyna speciosa) native to Southeast Asia that has been consumed for its complex stimulant-like effects at low doses, opiate-like effects at high doses, to treat mood related issues like anxiety or depression, or to help ameliorate opioid withdrawal symptoms. However, the neural mechanisms of its major psychoactive alkaloids, mitragynine (MG) and 7-hydroxymitragynine (7-HMG), are still not clear. Given that the effects of kratom are often compared to drugs with abuse liabilities, the current study examined the effects of MG and 7-HMG on reward-related neurotransmission. Fixed potential amperometry was used to quantify stimulation-evoked phasic dopamine release in the nucleus accumbens (NAc) of anesthetized male and female mice before and after MG (1, 15, or 30 mg/kg i.p.), 7-HMG (0.5, 1, or 2 mg/kg i.p.), or vehicle. MG reduced dopamine release over the recording period (90 min) in a dose dependent manner, and the low dose of MG significantly increased dopamine autoreceptor functioning in males. Both sexes responded similarly to 7-HMG with the low dose of 7-HMG increasing dopamine release while the high dose decreased dopamine release. 7-HMG did not alter dopamine autoreceptor functioning for either sex. Neither MG nor 7-HMG altered the clearance rate of stimulation-evoked dopamine. Findings suggest that these kratom alkaloids do alter dopamine functioning, although potentially not in a way consistent with classic drugs of abuse. Further investigation of the neural mechanisms of kratom’s alkaloids will provide crucial and urgent insight into their therapeutic uses or potential abuse liability. 

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