This report describes the setup and results of the shared task of human-like long story generation, the LSG Challenge, which asks to generate a consistent, human-like long story (a Harry Potter fanfic in English for a general audience) given a prompt of about 1,000 tokens. We evaluated the submissions using both automated metrics and human evaluation protocols. The automated metrics, including the GAPELMAPER score, assessed the structuredness of the generated texts, while human annotators rated stories on dimensions such as relevance, consistency, fluency, and coherence. Additionally, annotators evaluated the models’ understanding of abstract concepts, causality, the logical order of events, and the avoidance of repeated plot elements. The results highlight the current strengths and limitations of state-of-the-art models in long-form story generation, with key challenges emerging in maintaining coherence over extended narratives and handling complex story dynamics. Our analysis provides insights into future directions for improving long story generation systems.

This paper describes the TSU HITS team’s submission system for the WMT’24 general translation task. We focused on exploring the capabilities of discrete diffusion models for the English-to-{Russian, German, Czech, Spanish} translation tasks in the constrained track. Our submission system consists of a set of discrete diffusion models for each language pair. The main advance is using a separate length regression model to determine the length of the output sequence more precisely.

We propose a shared task of human-like long story generation, LSG Challenge, that asks models to output a consistent human-like long story (a Harry Potter generic audience fanfic in English), given a prompt of about 1K tokens. We suggest a novel statistical metric of the text structuredness, GloVe Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio (GAPELMAPER) and the use of previously-known UNION metric and a human evaluation protocol. We hope that LSG can open new avenues for researchers to investigate sampling approaches, prompting strategies, autoregressive and non-autoregressive text generation architectures and break the barrier to generate consistent long (40K+ word) texts.

This paper documents the approach of Team NTR for the Second Shared Task on Automatic Minuting (AutoMin) at INLG 2023. The goal of this work is to develop a module for automatic generation of meeting minutes based on a meeting transcript text produced by an Automated Speech Recognition (ASR) system (Task A). We consider minuting as a supervised machine learning task on pairs of texts: the transcript of the meeting and its minutes. We use a two-staged minuting pipeline that consists of segmentation and summarization. We experiment with semantic segmentation and multi-language approaches and Large Language Model Dolly, and achieve Rouge1-F of 0.2455 and BERT-Score of 0.8063 on the English part of ELITR test set and Rouge1-F of 0.2430 and BERT-Score of 0.8332 on the EuroParl dev set with the submitted Naive Segmentation + Dolly7b pipeline.

This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. For many low-resource and endangered languages, only single-speaker recordings may be available, demanding a need for domain and speaker-invariant language ID systems. In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results for the language identification task.