In this paper, we propose a novel evaluation paradigm for Targeted Syntactic Evaluations, where we assess how well language models can recognize linguistic phenomena situated at different levels of the Chomsky hierarchy. Specifically, we create formal languages that abstract four syntactic phenomena in natural languages, each identified at a different level of the Chomsky hierarchy, and use these to evaluate the capabilities of language models: (1) (Adj)ˆn NP type, (2) NPˆn VPˆn type, (3) Nested Dependency type, and (4) Cross Serial Dependency type. We first train three different language models (LSTM, Transformer LM, and Stack-RNN) on language modeling tasks and then evaluate them using pairs of a positive and a negative sentence by investigating whether they can assign a higher probability to the positive sentence than the negative one. Our result demonstrated that all language models have the ability to capture the structural patterns of the (Adj)ˆn NP type formal language. However, LSTM and Transformer LM failed to capture NPˆn VPˆn type language and no architectures can recognize nested dependency and Cross Serial dependency correctly. Neural language models, especially Transformer LMs, have exhibited high performance across a multitude of downstream tasks, leading to the perception that they possess an understanding of natural languages. However, our findings suggest that these models may not necessarily comprehend the syntactic structures that underlie natural language phenomena such as dependency. Rather, it appears that they may extend grammatical rules equivalent to regular grammars to approximate the rules governing dependencies.

In this paper, we propose a novel architecture called Composition Attention Grammars (CAGs) that recursively compose subtrees into a single vector representation with a composition function, and selectively attend to previous structural information with a self-attention mechanism. We investigate whether these components—the composition function and the self-attention mechanism—can both induce human-like syntactic generalization. Specifically, we train language models (LMs) with and without these two components with the model sizes carefully controlled, and evaluate their syntactic generalization performance against six test circuits on the SyntaxGym benchmark. The results demonstrated that the composition function and the self-attention mechanism both play an important role to make LMs more human-like, and closer inspection of linguistic phenomenon implied that the composition function allowed syntactic features, but not semantic features, to percolate into subtree representations.

In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final left-branching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models. Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and left-corner architectures are more cognitively plausible than top-down or sequential architectures. In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed.

In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization —the lower perplexity a language model has, the more human-like the language model is— in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a cross-lingual evaluation will be necessary to construct human-like computational models.