We demonstrate that coreference resolution in procedural texts is significantly improved when performing transformation-based entity linking prior to coreference relation identification. When events in the text introduce changes to the state of participating entities, it is often impossible to accurately link entities in anaphoric and coreference relations without an understanding of the transformations those entities undergo. We show how adding event semantics helps to better model entity coreference. We argue that all transformation predicates, not just creation verbs, introduce a new entity into the discourse, as a kind of generalized Result Role, which is typically not textually mentioned. This allows us to model procedural texts as process graphs and to compute the coreference type for any two entities in the recipe. We present our annotation methodology and the corpus generated as well as describe experiments on coreference resolution of entity mentions under a process-oriented model of events.

Understanding inferences from text requires more than merely recovering surface arguments, adjuncts, or strings associated with the query terms. As humans, we interpret sentences as contextualized components of a narrative or discourse, by both filling in missing information, and reasoning about event consequences. In this paper, we define the process of rewriting a textual expression (lexeme or phrase) such that it reduces ambiguity while also making explicit the underlying semantics that is not (necessarily) expressed in the economy of sentence structure as Dense Paraphrasing (DP). We apply the DP techniques on the English procedural texts from the cooking recipe domain, and provide the scope and design of the application that involves creating a graph representation of events and generating hidden arguments through paraphrasing. We provide insights on how this DP process can enrich a source text by showing that the dense-paraphrased event graph is a good resource to large LLMs such as GPT-3 to generate reliable paraphrases; and by experimenting baselines for automaticDP generation. Finally, we demonstrate the utility of the dataset and event graph structure by providing a case study on the out-of-domain modeling and different DP prompts and GPT models for paraphrasing.

The Computational Linguistics Applications for Multimedia Services (CLAMS) platform provides access to computational content analysis tools for multimedia material. The version we present here is a robust update of an initial prototype implementation from 2019. The platform now sports a variety of image, video, audio and text processing tools that interact via a common multi-modal representation language named MMIF (Multi-Media Interchange Format). We describe the overall architecture, the MMIF format, some of the tools included in the platform, the process to set up and run a workflow, visualizations included in CLAMS, and evaluate aspects of the platform on data from the American Archive of Public Broadcasting, showing how CLAMS can add metadata to mass-digitized multimedia collections, metadata that are typically only available implicitly in now largely unsearchable digitized media in archives and libraries.

Models of natural language understanding often rely on question answering and logical inference benchmark challenges to evaluate the performance of a system. While informative, such task-oriented evaluations do not assess the broader semantic abilities that humans have as part of their linguistic competence when speaking and interpreting language. We define competence-based (CB) question generation, and focus on queries over lexical semantic knowledge involving implicit argument and subevent structure of verbs. We present a method to generate such questions and a dataset of English cooking recipes we use for implementing the generation method. Our primary experiment shows that even large pretrained language models perform poorly on CB questions until they are provided with additional contextualized semantic information. The data and the source code is available at: https: //github.com/brandeis-llc/CompQG.

In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning. Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects. The task is to answer questions from a collection of cooking recipes and videos, where each question belongs to a “question family” reflecting a specific reasoning competence. The data and task result is publicly available.

Within the natural language processing (NLP) community, shared tasks play an important role. They define a common goal and allowthe the comparison of different methods on the same data. SemEval-2018 Task 7 involves the identification and classification of relationsin abstracts from computational linguistics (CL) publications. In this paper we describe an attempt to reproduce the methods and resultsfrom the top performing system at for SemEval-2018 Task 7. We describe challenges we encountered in the process, report on the resultsof our system, and discuss the ways that our attempt at reproduction can inform best practices.

Promoting interoperrable computational linguistics (CL) and natural language processing (NLP) application platforms and interchange-able data formats have contributed improving discoverabilty and accessbility of the openly available NLP software. In this paper, wediscuss the enhanced data visualization capabilities that are also enabled by inter-operating NLP pipelines and interchange formats. For adding openly available visualization tools and graphical annotation tools to the Language Applications Grid (LAPPS Grid) andComputational Linguistics Applications for Multimedia Services (CLAMS) toolboxes, we have developed interchange formats that cancarry annotations and metadata for text and audiovisual source data. We descibe those data formats and present case studies where wesuccessfully adopt open-source visualization tools and combine them with CL tools.

We present Computational Linguistics Applications for Multimedia Services (CLAMS), a platform that provides access to computational content analysis tools for archival multimedia material that appear in different media, such as text, audio, image, and video. The primary goal of CLAMS is: (1) to develop an interchange format between multimodal metadata generation tools to ensure interoperability between tools; (2) to provide users with a portable, user-friendly workflow engine to chain selected tools to extract meaningful analyses; and (3) to create a public software development kit (SDK) for developers that eases deployment of analysis tools within the CLAMS platform. CLAMS is designed to help archives and libraries enrich the metadata associated with their mass-digitized multimedia collections, that would otherwise be largely unsearchable.