Coreference resolution is the task of identifying and grouping mentions referring to the same real-world entity. Previous neural models have mainly focused on learning span representations and pairwise scores for coreference decisions. However, current methods do not explicitly capture the referential choice in the hierarchical discourse, an important factor in coreference resolution. In this study, we propose a new approach that incorporates rhetorical information into neural coreference resolution models. We collect rhetorical featuresfrom automated discourse parses and examine their impact. As a base model, we implement an end-to-end span-based coreference resolver using a partially fine-tuned multilingual entity-aware language model LUKE. We evaluate our method on the RuCoCo-23 Shared Task for coreference resolution in Russian. Our best model employing rhetorical distance between mentions has ranked 1st on the development set (74.6% F1) and 2nd on the test set (73.3% F1) of the Shared Task¹. We hope that our work will inspire further research on incorporating discourse information in neural coreference resolution models.
The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts. The task is to predict sentiment towards a named entity in a single sentence. The dataset for RuSentNE-2023 evaluation is based on the Russian news corpus RuSentNE having rich sentiment-related annotation. The corpus is annotated with named entities and sentiments towards these entities, along with related effects and emotional states. The evaluation was organized using the CodaLab competition framework. The main evaluation measure was macro-averaged measure of positive and negative classes. The best results achieved were of 66% Macro F-measure (Positive+Negative classes). We also tested ChatGPT on the test set from our evaluation and found that the zero-shot answers provided by ChatGPT reached 60% of the F-measure, which corresponds to 4th place in the evaluation. ChatGPT also provided detailed explanations of its conclusion. This can be considered as quite high for zero-shot application.
We show that the laws of autocorrelations decay in texts are closely related to applicability limits of language models. Using distributional semantics we empirically demonstrate that autocorrelations of words in texts decay according to a power law. We show that distributional semantics provides coherent autocorrelations decay exponents for texts translated to multiple languages. The autocorrelations decay in generated texts is quantitatively and often qualitatively different from the literary texts. We conclude that language models exhibiting Markov behavior, including large autoregressive language models, may have limitations when applied to long texts, whether analysis or generation.
The paper is a corpus study of Russian frequency adverbs chasto ‘frequently’, zachastuju ‘often’, redko ‘rarely’, izredka ‘rarely’, etc. In Russian lexicographic tradition, frequency adverbs either lack separate entries and are explained via references to their adjectival counterparts or are treated exclusively as denotations of intervals between events. As our study demonstrates, this covers only a small fraction of their actual corpus usage. Many frequency adverbs can quantify over subjects, and thus resemble classical quantifiers such as ‘many’ or ‘few’. Even when frequency adverbs quantify over predicates, they mostly refer not to intervals between events, but merely to their number. In some cases, they quantify over aspects of events, expressed by adjectives. There are also other important properties of Russian frequency adverbs missed by the dictionaries yet revealed by corpus analysis. Most frequency adverbs have a strong preference for topic or focus position, as motivated by their semantics. Some adverbs are preferable in generalized contexts, while others refer to specific events. Certain adverbs describe violations of the norm or undesirable events. Different adverbs quantify over different time periods: while some require a long time period, others may focus on very short stretches of time.
We investigated communicative reactions of people (N = 46), while telling stories to two companion robots, who reacted differently to the human gaze (head turning). In response to a human gaze the “aversive” robot averted its gaze away from the user, while the “responsive” robot, lifted its head and showed a responsive gaze. We found that users with high level of emotional intelligence prefer the gaze responsive robot and better recognize the difference between the robots. Thus, these users constitute the core group for the technology. In this paper, we further examine behavioral patterns of people in the experiment situation: (a) shift of attention to the story; (b) shift of attention to the robot; (c) joint attention. We also distinguish the communicative reactions of people, mainly from the core group, to the aversive and responsive gazes of the robots: positive responses to gaze contact and negative responses to gaze aversion. We show that for some users the responsive gaze behavior of the robot may serve as positive feedback, increasing the number of human iconic gestures, while telling a story to the responsive robot, and decreasing the number of iconic gestures in a story to the aversive robot.
