Papers by Xiulin Yang
Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive Ziji (2025.coling-main)
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| Challenge: | Existing language models tend to rely heavily on sequential cues, but not always favoring the closest strings. |
| Approach: | They construct a dataset of 320 synthetic sentences and 360 natural sentences from the BCC corpus . they evaluate 21 language models against this dataset and compare their performance to native Mandarin speakers . |
| Outcome: | The proposed models do not replicate human-like judgments in Mandarin Chinese . the results show that existing models tend to rely heavily on sequential cues . |
Scope-enhanced Compositional Semantic Parsing for DRT (2024.emnlp-main)
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| Challenge: | Existing compositional semantic parsers for DRT struggle to produce well-formed representations due to the complexity of the sentence. |
| Approach: | They propose a compositional, neurosymbolic semantic parser for DRT that uses a novel mechanism for predicting quantifier scope. |
| Outcome: | The proposed model produces well-formed outputs and performs well on complex sentences. |
Function Words as Statistical Cues for Language Learning (2026.acl-long)
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| Challenge: | Existing studies have argued that function words aid learning abstract grammatical knowledge from linear input. |
| Approach: | They examine the statistical distribution of function words and their properties . they show that function words are reliable, diverse, and informative . |
| Outcome: | The results show that function words preserve high frequency, reliable syntactic association, phrase-boundary alignment and are informative to structural dependency. |
Anything Goes? A Crosslinguistic Study of (Im)possible Language Learning in LMs (2025.acl-long)
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| Challenge: | LMs are highly flexible learners, capable of acquiring linguistic patterns beyond those learnable by humans. |
| Approach: | They train LMs to model impossible and typologically unattested languages . they find that the model does not achieve perfect separation between attested and unattest languages - suggesting some human-like inductive biases . |
| Outcome: | The proposed model can largely distinguish attested from impossible languages, but does not achieve perfect separation between them and their impossible counterparts. |
A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)
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| Challenge: | Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence. |
| Approach: | They propose to use German Abstract Meaning Representation (Deutsche AMR) to represent the structure and semantics of German. |
| Outcome: | The proposed framework is based on an annotated corpus of 400 DeAMR in German and is validated through inter-annotator agreement. |