Parisa Kordjamshidi


2021

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Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics
Malihe Alikhani | Valts Blukis | Parisa Kordjamshidi | Aishwarya Padmakumar | Hao Tan
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

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Towards Navigation by Reasoning over Spatial Configurations
Yue Zhang | Quan Guo | Parisa Kordjamshidi
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

We deal with the navigation problem where the agent follows natural language instructions while observing the environment. Focusing on language understanding, we show the importance of spatial semantics in grounding navigation instructions into visual perceptions. We propose a neural agent that uses the elements of spatial configurations and investigate their influence on the navigation agent’s reasoning ability. Moreover, we model the sequential execution order and align visual objects with spatial configurations in the instruction. Our neural agent improves strong baselines on the seen environments and shows competitive performance on the unseen environments. Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.

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Zero-Shot Compositional Concept Learning
Guangyue Xu | Parisa Kordjamshidi | Joyce Chai
Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing

In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate conceptvisual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more indepth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the ransductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem. Experiments on two widelyused zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.

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Time-Stamped Language Model: Teaching Language Models to Understand The Flow of Events
Hossein Rajaby Faghihi | Parisa Kordjamshidi
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding. Secondly, since the transformer-based language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model (TSLM) to encode event information in LMs architecture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a 3.1% increase in F1 score. Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general.

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SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
Roshanak Mirzaee | Hossein Rajaby Faghihi | Qiang Ning | Parisa Kordjamshidi
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reasoning rules to automatically generate a spatial description of visual scenes and corresponding QA pairs. Experiments show that further pretraining LMs on these automatically generated data significantly improves LMs’ capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster investigations into more sophisticated models for spatial reasoning over text.

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DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning
Hossein Rajaby Faghihi | Quan Guo | Andrzej Uszok | Aliakbar Nafar | Parisa Kordjamshidi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We demonstrate a library for the integration of domain knowledge in deep learning architectures. Using this library, the structure of the data is expressed symbolically via graph declarations and the logical constraints over outputs or latent variables can be seamlessly added to the deep models. The domain knowledge can be defined explicitly, which improves the explainability of the models in addition to their performance and generalizability in the low-data regime. Several approaches for such integration of symbolic and sub-symbolic models have been introduced; however, there is no library to facilitate the programming for such integration in a generic way while various underlying algorithms can be used. Our library aims to simplify programming for such integration in both training and inference phases while separating the knowledge representation from learning algorithms. We showcase various NLP benchmark tasks and beyond. The framework is publicly available at Github(https://github.com/HLR/DomiKnowS).

2020

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From Spatial Relations to Spatial Configurations
Soham Dan | Parisa Kordjamshidi | Julia Bonn | Archna Bhatia | Zheng Cai | Martha Palmer | Dan Roth
Proceedings of the 12th Language Resources and Evaluation Conference

Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos.Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation (AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

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SRLGRN: Semantic Role Labeling Graph Reasoning Network
Chen Zheng | Parisa Kordjamshidi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

This work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly. The proposed graph is a heterogeneous document-level graph that contains nodes of type sentence (question, title, and other sentences), and semantic role labeling sub-graphs per sentence that contain arguments as nodes and predicates as edges. Incorporating the argument types, the argument phrases, and the semantics of the edges originated from SRL predicates into the graph encoder helps in finding and also the explainability of the reasoning paths. Our proposed approach shows competitive performance on the HotpotQA distractor setting benchmark compared to the recent state-of-the-art models.

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Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks
Parisa Kordjamshidi | James Pustejovsky | Marie-Francine Moens
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Understating spatial semantics expressed in natural language can become highly complex in real-world applications. This includes applications of language grounding, navigation, visual question answering, and more generic human-machine interaction and dialogue systems. In many of such downstream tasks, explicit representation of spatial concepts and relationships can improve the capabilities of machine learning models in reasoning and deep language understanding. In this tutorial, we overview the cutting-edge research results and existing challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. We discuss the recent results on the above-mentioned applications –that need spatial language learning and reasoning – and highlight the research gaps and future directions.

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Proceedings of the Third International Workshop on Spatial Language Understanding
Parisa Kordjamshidi | Archna Bhatia | Malihe Alikhani | Jason Baldridge | Mohit Bansal | Marie-Francine Moens
Proceedings of the Third International Workshop on Spatial Language Understanding

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Cross-Modality Relevance for Reasoning on Language and Vision
Chen Zheng | Quan Guo | Parisa Kordjamshidi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR). We design a novel cross-modality relevance module that is used in an end-to-end framework to learn the relevance representation between components of various input modalities under the supervision of a target task, which is more generalizable to unobserved data compared to merely reshaping the original representation space. In addition to modeling the relevance between the textual entities and visual entities, we model the higher-order relevance between entity relations in the text and object relations in the image. Our proposed approach shows competitive performance on two different language and vision tasks using public benchmarks and improves the state-of-the-art published results. The learned alignments of input spaces and their relevance representations by NLVR task boost the training efficiency of VQA task.

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Latent Alignment of Procedural Concepts in Multimodal Recipes
Hossein Rajaby Faghihi | Roshanak Mirzaee | Sudarshan Paliwal | Parisa Kordjamshidi
Proceedings of the First Workshop on Advances in Language and Vision Research

We propose a novel alignment mechanism to deal with procedural reasoning on a newly released multimodal QA dataset, named RecipeQA. Our model is solving the textual cloze task which is a reading comprehension on a recipe containing images and instructions. We exploit the power of attention networks, cross-modal representations, and a latent alignment space between instructions and candidate answers to solve the problem. We introduce constrained max-pooling which refines the max pooling operation on the alignment matrix to impose disjoint constraints among the outputs of the model. Our evaluation result indicates a 19% improvement over the baselines.

2019

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Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)
Archna Bhatia | Yonatan Bisk | Parisa Kordjamshidi | Jesse Thomason
Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)

2018

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Visually Guided Spatial Relation Extraction from Text
Taher Rahgooy | Umar Manzoor | Parisa Kordjamshidi
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Extraction of spatial relations from sentences with complex/nesting relationships is very challenging as often needs resolving inherent semantic ambiguities. We seek help from visual modality to fill the information gap in the text modality and resolve spatial semantic ambiguities. We use various recent vision and language datasets and techniques to train inter-modality alignment models, visual relationship classifiers and propose a novel global inference model to integrate these components into our structured output prediction model for spatial role and relation extraction. Our global inference model enables us to utilize the visual and geometric relationships between objects and improves the state-of-art results of spatial information extraction from text.

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Proceedings of the First International Workshop on Spatial Language Understanding
Parisa Kordjamshidi | Archna Bhatia | James Pustejovsky | Marie-Francine Moens
Proceedings of the First International Workshop on Spatial Language Understanding

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Anaphora Resolution for Improving Spatial Relation Extraction from Text
Umar Manzoor | Parisa Kordjamshidi
Proceedings of the First International Workshop on Spatial Language Understanding

Spatial relation extraction from generic text is a challenging problem due to the ambiguity of the prepositions spatial meaning as well as the nesting structure of the spatial descriptions. In this work, we highlight the difficulties that the anaphora can make in the extraction of spatial relations. We use external multi-modal (here visual) resources to find the most probable candidates for resolving the anaphoras that refer to the landmarks of the spatial relations. We then use global inference to decide jointly on resolving the anaphora and extraction of the spatial relations. Our preliminary results show that resolving anaphora improves the state-of-the-art results on spatial relation extraction.

2017

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Spatial Language Understanding with Multimodal Graphs using Declarative Learning based Programming
Parisa Kordjamshidi | Taher Rahgooy | Umar Manzoor
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing

This work is on a previously formalized semantic evaluation task of spatial role labeling (SpRL) that aims at extraction of formal spatial meaning from text. Here, we report the results of initial efforts towards exploiting visual information in the form of images to help spatial language understanding. We discuss the way of designing new models in the framework of declarative learning-based programming (DeLBP). The DeLBP framework facilitates combining modalities and representing various data in a unified graph. The learning and inference models exploit the structure of the unified graph as well as the global first order domain constraints beyond the data to predict the semantics which forms a structured meaning representation of the spatial context. Continuous representations are used to relate the various elements of the graph originating from different modalities. We improved over the state-of-the-art results on SpRL.

2016

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EDISON: Feature Extraction for NLP, Simplified
Mark Sammons | Christos Christodoulopoulos | Parisa Kordjamshidi | Daniel Khashabi | Vivek Srikumar | Dan Roth
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications.

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Better call Saul: Flexible Programming for Learning and Inference in NLP
Parisa Kordjamshidi | Daniel Khashabi | Christos Christodoulopoulos | Bhargav Mangipudi | Sameer Singh | Dan Roth
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul’s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.

2015

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SemEval-2015 Task 8: SpaceEval
James Pustejovsky | Parisa Kordjamshidi | Marie-Francine Moens | Aaron Levine | Seth Dworman | Zachary Yocum
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Improving a Pipeline Architecture for Shallow Discourse Parsing
Yangqiu Song | Haoruo Peng | Parisa Kordjamshidi | Mark Sammons | Dan Roth
Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task

2014

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HiEve: A Corpus for Extracting Event Hierarchies from News Stories
Goran Glavaš | Jan Šnajder | Marie-Francine Moens | Parisa Kordjamshidi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In news stories, event mentions denote real-world events of different spatial and temporal granularity. Narratives in news stories typically describe some real-world event of coarse spatial and temporal granularity along with its subevents. In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events. In HiEve, the narratives are represented as hierarchies of events based on relations of spatiotemporal containment (i.e., superevent―subevent relations). We describe the process of manual annotation of HiEve. Furthermore, we build a supervised classifier for recognizing spatiotemporal containment between events to serve as a baseline for future research. Preliminary experimental results are encouraging, with classifier performance reaching 58% F1-score, only 11% less than the inter annotator agreement.

2013

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SemEval-2013 Task 3: Spatial Role Labeling
Oleksandr Kolomiyets | Parisa Kordjamshidi | Marie-Francine Moens | Steven Bethard
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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SemEval-2012 Task 3: Spatial Role Labeling
Parisa Kordjamshidi | Steven Bethard | Marie-Francine Moens
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

2010

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Spatial Role Labeling: Task Definition and Annotation Scheme
Parisa Kordjamshidi | Martijn Van Otterlo | Marie-Francine Moens
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

One of the essential functions of natural language is to talk about spatial relationships between objects. Linguistic constructs can express highly complex, relational structures of objects, spatial relations between them, and patterns of motion through spaces relative to some reference point. Learning how to map this information onto a formal representation from a text is a challenging problem. At present no well-defined framework for automatic spatial information extraction exists that can handle all of these issues. In this paper we introduce the task of spatial role labeling and propose an annotation scheme that is language-independent and facilitates the application of machine learning techniques. Our framework consists of a set of spatial roles based on the theory of holistic spatial semantics with the intent of covering all aspects of spatial concepts, including both static and dynamic spatial relations. We illustrate our annotation scheme with many examples throughout the paper, and in addition we highlight how to connect to spatial calculi such as region connection calculus and also how our approach fits into related work.