The rapid growth of deep learning has spurred significant advancements across industries, par- ticularly in machine translation through large language models (LLMs). However, translat- ing literary still presents challenges, including cross-cultural nuances, complex language struc- tures, metaphorical expressions, and cultural differences. To address these issues, this study utilizes the Llama and Phi models using both LoRA and full-parameter techniques, along-side a prompt-based translation system. Full-parameter tuning of the Llama-3-Chinese-8B-Instruct model was unsuccessful due to mem-ory constraints. In terms of the WMT task, the fully fine-tuned Phi 3 model was selected for submission due to its more natural and flu-ent translations. Nonetheless, results showed that LoRA and the prompt-based system sig- nificantly improved the Llama3 model’s perfor- mance, surpassing other models in BLEU and ROUGE evaluations.

Event-Level Video Question Answering (EVQA) requires complex reasoning across video events to obtain the visual information needed to provide optimal answers. However, despite significant progress in model performance, few studies have focused on using the explicit semantic connections between the question and visual information especially at the event level. There is need for using such semantic connections to facilitate complex reasoning across video frames. Therefore, we propose a semantic-aware dynamic retrospective-prospective reasoning approach for video-based question answering. Specifically, we explicitly use the Semantic Role Labeling (SRL) structure of the question in the dynamic reasoning process where we decide to move to the next frame based on which part of the SRL structure (agent, verb, patient, etc.) of the question is being focused on. We conduct experiments on a benchmark EVQA dataset - TrafficQA. Results show that our proposed approach achieves superior performance compared to previous state-of-the-art models. Our code is publicly available at https://github.com/lyuchenyang/Semantic-aware-VideoQA.

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking document-level machine translation (MT) as a testbed, this paper provides an in-depth evaluation of LLMs’ ability on discourse modeling. The study focuses on three aspects: 1) Effects of Context-Aware Prompts, where we investigate the impact of different prompts on document-level translation quality and discourse phenomena; 2) Comparison of Translation Models, where we compare the translation performance of ChatGPT with commercial MT systems and advanced document-level MT methods; 3) Analysis of Discourse Modelling Abilities, where we further probe discourse knowledge encoded in LLMs and shed light on impacts of training techniques on discourse modeling. By evaluating on a number of benchmarks, we surprisingly find that LLMs have demonstrated superior performance and show potential to become a new paradigm for document-level translation: 1) leveraging their powerful long-text modeling capabilities, GPT-3.5 and GPT-4 outperform commercial MT systems in terms of human evaluation; 2) GPT-4 demonstrates a stronger ability for probing linguistic knowledge than GPT-3.5. This work highlights the challenges and opportunities of LLMs for MT, which we hope can inspire the future design and evaluation of LLMs (We release our data and annotations at https://github.com/longyuewangdcu/Document-MT-LLM).

Evaluation of open-domain dialogue systems is highly challenging and development of better techniques is highlighted time and again as desperately needed. Despite substantial efforts to carry out reliable live evaluation of systems in recent competitions, annotations have been abandoned and reported as too unreliable to yield sensible results. This is a serious problem since automatic metrics are not known to provide a good indication of what may or may not be a high-quality conversation. Answering the distress call of competitions that have emphasized the urgent need for better evaluation techniques in dialogue, we present the successful development of human evaluation that is highly reliable while still remaining feasible and low cost. Self-replication experiments reveal almost perfectly repeatable results with a correlation of r=0.969. Furthermore, due to the lack of appropriate methods of statistical significance testing, the likelihood of potential improvements to systems occurring due to chance is rarely taken into account in dialogue evaluation, and the evaluation we propose facilitates application of standard tests. Since we have developed a highly reliable evaluation method, new insights into system performance can be revealed. We therefore include a comparison of state-of-the-art models (i) with and without personas, to measure the contribution of personas to conversation quality, as well as (ii) prescribed versus freely chosen topics. Interestingly with respect to personas, results indicate that personas do not positively contribute to conversation quality as expected.

In this paper, we describe our system for the FinNLP-2022 shared task: Evaluating the Rationales of Amateur Investors (ERAI). The ERAI shared tasks focuses on mining profitable information from financial texts by predicting the possible Maximal Potential Profit (MPP) and Maximal Loss (ML) based on the posts from amateur investors. There are two sub-tasks in ERAI: Pairwise Comparison and Unsupervised Rank, both target on the prediction of MPP and ML. To tackle the two tasks, we frame this task as a text-pair classification task where the input consists of two documents and the output is the label of whether the first document will lead to higher MPP or lower ML. Specifically, we propose to take advantage of the transferability of Sentiment Analysis data with an assumption that a more positive text will lead to higher MPP or higher ML to facilitate the prediction of MPP and ML. In experiment on the ERAI blind test set, our systems trained on Sentiment Analysis data and ERAI training data ranked 1st and 8th in ML and MPP pairwise comparison respectively. Code available in this link.

In this paper, we describe our DCU-Lorcan system for the FinCausal 2022 shared task: span-based cause and effect extraction from financial documents. We frame the FinCausal 2022 causality extraction task as a span extraction/sequence labeling task, our submitted systems are based on the contextualized word representations produced by pre-trained language models and linear layers predicting the label for each word, followed by post-processing heuristics. In experiments, we employ pre-trained language models including DistilBERT, BERT and SpanBERT. Our best performed system achieves F-1, Recall, Precision and Exact Match scores of 92.76, 92.77, 92.76 and 68.60 respectively. Additionally, we conduct experiments investigating the effect of data size to the performance of causality extraction model and an error analysis investigating the outputs in predictions.

In this paper, we present our system for the EvoNLP 2022 shared task Temporal Meaning Shift (TempoWiC). Different from the typically used discriminative model, we propose a generative approach based on pre-trained generation models. The basic architecture of our system is a seq2seq model where the input sequence consists of two documents followed by a question asking whether the meaning of target word changed or not, the target output sequence is a declarative sentence describing the meaning of target word changed or not. The experimental results on TempoWiC test set show that our best system (with time information) obtained an accuracy and Marco F-1 score of 68.09% and 62.59% respectively, which ranked 12th among all submitted systems. The results have shown the plausibility of using generation model for WiC tasks, meanwhile also indicate there’s still room for further improvement.