Mohsen Mesgar


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A Neural Graph-based Local Coherence Model
Mohsen Mesgar | Leonardo F. R. Ribeiro | Iryna Gurevych
Findings of the Association for Computational Linguistics: EMNLP 2021

Entity grids and entity graphs are two frameworks for modeling local coherence. These frameworks represent entity relations between sentences and then extract features from such representations to encode coherence. The benefits of convolutional neural models for extracting informative features from entity grids have been recently studied. In this work, we study the benefits of Relational Graph Convolutional Networks (RGCN) to encode entity graphs for measuring local coherence. We evaluate our neural graph-based model for two benchmark coherence evaluation tasks: sentence ordering (SO) and summary coherence rating (SCR). The results show that our neural graph-based model consistently outperforms the neural grid-based model for both tasks. Our model performs competitively with a strong baseline coherence model, while our model uses 50% fewer parameters. Our work defines a new, efficient, and effective baseline for local coherence modeling.

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Improving Factual Consistency Between a Response and Persona Facts
Mohsen Mesgar | Edwin Simpson | Iryna Gurevych
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker’s persona. These models are trained with fully supervised learning where the objective function barely captures factual consistency. We propose to fine-tune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility. Our automatic and human evaluations on the PersonaChat corpus confirm that our approach increases the rate of responses that are factually consistent with persona facts over its supervised counterpart while retains the language quality of responses.


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Dialogue Coherence Assessment Without Explicit Dialogue Act Labels
Mohsen Mesgar | Sebastian Bücker | Iryna Gurevych
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent dialogue coherence models use the coherence features designed for monologue texts, e.g. nominal entities, to represent utterances and then explicitly augment them with dialogue-relevant features, e.g., dialogue act labels. It indicates two drawbacks, (a) semantics of utterances are limited to entity mentions, and (b) the performance of coherence models strongly relies on the quality of the input dialogue act labels. We address these issues by introducing a novel approach to dialogue coherence assessment. We use dialogue act prediction as an auxiliary task in a multi-task learning scenario to obtain informative utterance representations for coherence assessment. Our approach alleviates the need for explicit dialogue act labels during evaluation. The results of our experiments show that our model substantially (more than 20 accuracy points) outperforms its strong competitors on the DailyDialogue corpus, and performs on par with them on the SwitchBoard corpus for ranking dialogues concerning their coherence. We release our source code.


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Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Steffen Eger | Gözde Gül Şahin | Andreas Rücklé | Ji-Ung Lee | Claudia Schulz | Mohsen Mesgar | Krishnkant Swarnkar | Edwin Simpson | Iryna Gurevych
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., “!d10t”) or as a writing style (“1337” in “leet speak”), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual perturbations demonstrate. We investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82%. We then explore three shielding methods—visual character embeddings, adversarial training, and rule-based recovery—which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.


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A Neural Local Coherence Model for Text Quality Assessment
Mohsen Mesgar | Michael Strube
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other task-dependent features significantly improves the performance of a strong essay scorer.


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Using a Graph-based Coherence Model in Document-Level Machine Translation
Leo Born | Mohsen Mesgar | Michael Strube
Proceedings of the Third Workshop on Discourse in Machine Translation

Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube, (2016) with Docent (Hardmeier et al., 2012, Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.


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Generating Coherent Summaries of Scientific Articles Using Coherence Patterns
Daraksha Parveen | Mohsen Mesgar | Michael Strube
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Lexical Coherence Graph Modeling Using Word Embeddings
Mohsen Mesgar | Michael Strube
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Feature-Rich Error Detection in Scientific Writing Using Logistic Regression
Madeline Remse | Mohsen Mesgar | Michael Strube
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications


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Graph-based Coherence Modeling For Assessing Readability
Mohsen Mesgar | Michael Strube
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics


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Normalized Entity Graph for Computing Local Coherence
Mohsen Mesgar | Michael Strube
Proceedings of TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing


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History Based Unsupervised Data Oriented Parsing
Mohsen Mesgar | Gholamreza Ghasem-Sani
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013