Density Matching for Bilingual Word Embedding

Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.Comment: Accepted by NAACL-HLT 201

Learning for the next pandemic: when high level evidence is not readily available...

Coronavirus disease 2019 (COVID-19) represents the most severe pandemic since the 1918 pandemic of Spanish flu. As of February 11th 2022, there have been 404,910,528 laboratory-confirmed cases of COVID-19, including 5,783,776 deaths reported to the World Health Organization (WHO) (1). Approximately 5% of hospitalised patients with COVID-19 have been classified as critical cases due to the presence of severe respiratory failure and/or multiple organ dysfunction (2), for whom treatment is still supportive rather than definitive

Development of Novel Methods of Analysis for Indoor Air Pollutants

The current variability and speciation of indoor VOCs are studied by analysing indoor air in UK homes and offices. These measurements were carried out via passive sampling into silica-treated canisters followed by thermal desorption-gas chromatography and high mass accuracy time-of-flight mass spectrometry (TD-GC-Q-TOF/MS). It was found that majority of the homes had d-limonene and α-pinene as the most abundant VOCs, with average concentrations ranging from 18 μg m-3 to over 1400 μg m-3 and 2 μg m-3 to 230 μg m-3 respectively. In these analyses, cyclic volatile methyl siloxanes (cVMS) were frequently detected in high abundances. cVMS are chemicals in high volume production as they are used as solvents in formulations of consumer products. They were found in persistently high background concentrations in our analyses. Hence, a passive sampling method involving sorbents was developed to allow the analysis and quantification of these compounds, with LODs calculated to be 7.2 to 16.8 ng m-3. This method was validated with real indoor air sampling with average D5 and D6 concentrations of about 2480 ng m-3 and 664 ng m-3 respectively. Advancements have also been made in the development of a multispecies sensor for the detection of VOCs. A temperature control method was developed using a Peltier device and a control software programme written in LABVIEW. Attempts were made to manufacture a lab-on-a-chip GC column, but was deemed unsuitable due to leakage and mechanical problems. Instead, a short length of column was wound and placed in a copper enclosure. Tests were conducted using photoionsation detector (PID) as the detection method in this sensor development. The final set-up involved the assembly of the temperature control method, the GC column enclosure and the PID for the detection. Tests were conducted by introducing headspace standards into the set-up, with promising results

Regulation and Transcendence: The Policy Guarantee Provided for Tuition-Free Normal University Students’ Career Aspiration Education

The state tuition-free policy for normal university students has given basic requirements for the career aspiration education on the part of tuition-free normal university students, and has served as an important reason for effectively pushing forward tuition-free normal university students’ career aspiration education. However, during the process of implementing this policy, many problems crop up. For example, the policy itself is not complete; the implementation is not effective; and there are not enough support and recognition for this policy. All these, to a certain extent, have hindered the realization of students’ career aspirations to become outstanding teachers and educators. Therefore, we should continue to improve the state tuition-free policy for normal university students, strengthen the effectiveness of its implementation, and enhance students’ recognition for this policy, thus achieving an organic unity of national needs, social needs and students’ personal needs, as well as constructing good policy guarantee for the career aspiration education on the part of normal university students who are receiving free education

A Study on Choosing Paths to Conducting Career Aspiration Education on the Part of Tuition-Free Normal University Students

Establishing a firm and lofty career aspiration on the part of tuition-free university students is the key to the effective implementation of the policy of free normal education, and it is the heart and soul of cultivating tuition-free normal university students. If effective paths to conducting career aspiration education are secured, tuition-free normal university students will receive a lot of assistance to correctly analyze themselves, understand the teaching profession in depth, get to know the society comprehensively, and achieve their career aspirations. Effective paths to conducting career aspiration education are mainly as follows: improving national supporting policies, developing professional courses, organizing activities to train students’ capabilities, establishing advanced and typical models, and creating atmosphere of respecting teachers and valuing teaching

MART: Improving LLM Safety with Multi-round Automatic Red-Teaming

Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressing them with responsible and accurate responses. While effective, manual red-teaming is costly, and existing automatic red-teaming typically discovers safety risks without addressing them. In this paper, we propose a Multi-round Automatic Red-Teaming (MART) method, which incorporates both automatic adversarial prompt writing and safe response generation, significantly increasing red-teaming scalability and the safety of the target LLM. Specifically, an adversarial LLM and a target LLM interplay with each other in an iterative manner, where the adversarial LLM aims to generate challenging prompts that elicit unsafe responses from the target LLM, while the target LLM is fine-tuned with safety aligned data on these adversarial prompts. In each round, the adversarial LLM crafts better attacks on the updated target LLM, while the target LLM also improves itself through safety fine-tuning. On adversarial prompt benchmarks, the violation rate of an LLM with limited safety alignment reduces up to 84.7% after 4 rounds of MART, achieving comparable performance to LLMs with extensive adversarial prompt writing. Notably, model helpfulness on non-adversarial prompts remains stable throughout iterations, indicating the target LLM maintains strong performance on instruction following