Sebastian Pado

Sean Papay, Roman Klinger, Sebastian Pado

While LLMs have grown popular in sequence labeling, linear-chain conditional random fields (CRFs) remain a popular alternative with the ability to directly model interactions between labels. However, the Markov assumption limits them to % only directly modeling interactions between adjacent labels. Weighted finite-state transducers (FSTs), in contrast, can model distant label--label interactions, but exact label inference is intractable in general. In this work, we present regular-pattern-sensitive CRFs (RPCRFs), a method of enriching standard linear-chain CRFs with the ability to learn long-distance label interactions through user-specified patterns. This approach allows users to write regular-expression label patterns concisely specifying which types of interactions the model should take into account, allowing the model to learn from data whether and in which contexts these patterns occur. The result can be interpreted alternatively as a CRF augmented with additional, non-local potentials, or as a finite-state transducer whose structure is defined by a set of easily-interpretable patterns. Critically, exact training and inference are tractable for many pattern sets. We detail how an RPCRF can be automatically constructed from a set of user-specified patterns, and demonstrate the model's effectiveness on a sequence of three synthetic sequence modeling datasets.

Sebastian Pado, Daniel Hole

In this paper, we are concerned with the phenomenon of function word polysemy. We adopt the framework of distributional semantics, which characterizes word meaning by observing occurrence contexts in large corpora and which is in principle well situated to model polysemy. Nevertheless, function words were traditionally considered as impossible to analyze distributionally due to their highly flexible usage patterns. We establish that contextualized word embeddings, the most recent generation of distributional methods, offer hope in this regard. Using the German reflexive pronoun 'sich' as an example, we find that contextualized word embeddings capture theoretically motivated word senses for 'sich' to the extent to which these senses are mirrored systematically in linguistic usage.

Abhijeet Gupta, Gemma Boleda, Sebastian Pado

In computational linguistics, a large body of work exists on distributed modeling of lexical relations, focussing largely on lexical relations such as hypernymy (scientist -- person) that hold between two categories, as expressed by common nouns. In contrast, computational linguistics has paid little attention to entities denoted by proper nouns (Marie Curie, Mumbai, ...). These have investigated in detail by the Knowledge Representation and Semantic Web communities, but generally not with regard to their linguistic properties. Our paper closes this gap by investigating and modeling the lexical relation of instantiation, which holds between an entity-denoting and a category-denoting expression (Marie Curie -- scientist or Mumbai -- city). We present a new, principled dataset for the task of instantiation detection as well as experiments and analyses on this dataset. We obtain the following results: (a), entities belonging to one category form a region in distributional space, but the embedding for the category word is typically located outside this subspace; (b) it is easy to learn to distinguish entities from categories from distributional evidence, but due to (a), instantiation proper is much harder to learn when using common nouns as representations of categories; (c) this problem can be alleviated by using category representations based on entity rather than category word embeddings.

Sebastian Pado, Mirella Lapata

This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data.