The field of Distributional Semantics has recently undergone important changes, with the contextual representations produced by Transformers taking the place of static word embeddings models. Noticeably, previous studies comparing the two types of vectors have only focused on the English language and a limited number of models. In our study, we present a comparative evaluation of static and contextualized distributional models for Mandarin Chinese, focusing on a range of intrinsic tasks. Our results reveal that static models remain stronger for some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and in the categorization of words into abstract semantic classes.
We explore the utility of pre-trained Large Language Models (LLMs) in detecting the presence, subtypes, and severity of aphasia across English and Mandarin Chinese speakers. Our investigation suggests that even without fine-tuning or domain-specific training, pre-trained LLMs can offer some insights on language disorders, regardless of speakers’ first language. Our analysis also reveals noticeable differences between English and Chinese LLMs. While the English LLMs exhibit near-chance level accuracy in subtyping aphasia, the Chinese counterparts demonstrate less than satisfactory performance in distinguishing between individuals with and without aphasia. This research advocates for the importance of linguistically tailored and specified approaches in leveraging LLMs for clinical applications, especially in the context of multilingual populations.
As neural language models (NLMs) based on Transformers are becoming increasingly dominant in natural language processing, several studies have proposed analyzing the semantic and pragmatic abilities of such models. In our study, we aimed at investigating the effect of discourse connectives on NLMs with regard to Transformer Surprisal scores by focusing on the English stimuli of an experimental dataset, in which the expectations about an event in a discourse fragment could be reversed by a concessive or a contrastive connective. By comparing the Surprisal scores of several NLMs, we found that bigger NLMs show patterns similar to humans’ behavioral data when a concessive connective is used, while connective-related effects tend to disappear with a contrastive one. We have additionally validated our findings with GPT-Neo using an extended dataset, and results mostly show a consistent pattern.
In psycholinguistics, semantic attraction is a sentence processing phenomenon in which a given argument violates the selectional requirements of a verb, but this violation is not perceived by comprehenders due to its attraction to another noun in the same sentence, which is syntactically unrelated but semantically sound. In our study, we use autoregressive language models to compute the sentence-level and the target phrase-level Surprisal scores of a psycholinguistic dataset on semantic attraction. Our results show that the models are sensitive to semantic attraction, leading to reduced Surprisal scores, although none of them perfectly matches the human behavioral pattern.
By saying Maria is tall, a human speaker typically implies that Maria is evaluatively tall from the speaker’s perspective. However, by using a different construction Maria is taller than Sophie, we cannot infer from Maria and Sophie’s relative heights that Maria is evaluatively tall because it is possible for Maria to be taller than Sophie in a context in which they both count as short. Can pre-trained language models (LMs) “understand” evaulativity (EVAL) inference? To what extent can they discern the EVAL salience of different constructions in a conversation? Will it help LMs’ implicitness performance if we give LMs a persona such as chill, social, and pragmatically skilled? Our study provides an approach to probing LMs’ interpretation of EVAL inference by incorporating insights from experimental pragmatics and sociolinguistics. We find that with the appropriate prompt, LMs can succeed in some pragmatic level language understanding tasks. Our study suggests that socio-pragmatics methodology can shed light on the challenging questions in NLP.
Neural language models have attracted a lot of attention in the past few years. More and more researchers are getting intrigued by how language models encode commonsense, specifically what kind of commonsense they understand, and why they do. This paper analyzed neural language models’ understanding of commonsense pragmatics (i.e., implied meanings) through human behavioral and neurophysiological data. These psycholinguistic tests are designed to draw conclusions based on predictive responses in context, making them very well suited to test word-prediction models such as BERT in natural settings. They can provide the appropriate prompts and tasks to answer questions about linguistic mechanisms underlying predictive responses. This paper adopted psycholinguistic datasets to probe language models’ commonsense reasoning. Findings suggest that GPT-3’s performance was mostly at chance in the psycholinguistic tasks. We also showed that DistillBERT had some understanding of the (implied) intent that’s shared among most people. Such intent is implicitly reflected in the usage of conversational implicatures and presuppositions. Whether or not fine-tuning improved its performance to human-level depends on the type of commonsense reasoning.
As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs’ competence in pragmatics, with a focus on pragmatics relating to discourse connectives. We formulate cloze-style tests using a combination of naturally-occurring data and controlled inputs drawn from psycholinguistics. We focus on testing models’ ability to use pragmatic cues to predict discourse connectives, models’ ability to understand implicatures relating to connectives, and the extent to which models show humanlike preferences regarding temporal dynamics of connectives. We find that although models predict connectives reasonably well in the context of naturally-occurring data, when we control contexts to isolate high-level pragmatic cues, model sensitivity is much lower. Models also do not show substantial humanlike temporal preferences. Overall, the findings suggest that at present, dominant pre-training paradigms do not result in substantial pragmatic competence in our models.