{
    "paper_id": "2020",
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        "paper_id": "2020",
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        "abstract": [],
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            {
                "text": "Welcome to LANTERN 2020, The Second Workshop Beyond Vision and Language: Integrating Real-World Knowledge, co-located with COLING 2020. Building on the success of the first edition co-located with EMNLP-IJCNLP 2019, the second edition aims to bring together and interconnect researchers focusing on language from a multimodal perspective. In particular, the main goal of the workshop is to promote and foster research which uses machine-and deep-learning techniques to interconnect language with vision and other modalities by leveraging some external knowledge. By encouraging contributions exploiting very diverse sources of external knowledge (knowledge graphs, fixed and dynamic environments, cognitive and neuroscience data, etc.), the workshop is open to all research directions which acknowledge the importance of knowledge in acquiring, using, and evaluating language in real-world settings.",
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                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "In this second edition, we called for both long and short papers. All the accepted contributions are published in these Proceedings. Moreover, we host presentations from papers accepted to appear in the novel \"Findings of EMNLP 2020\" series. These papers are not published in the LANTERN Proceedings.",
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                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "LANTERN 2020 received 7 submissions, all of which were double-blindly reviewed by three highlyqualified reviewers. In total, 4 papers (1 long, 3 short) were accepted to appear in the Proceedings of the workshop, with an acceptance rate of around 57% (comparable to that of the first edition, which was around 53%).",
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                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "Contributions are representative of a broad range of current problems and approaches and include a novel technique to improve text-to-image synthesis by leveraging visual question answering, an approach to learn commonsense from image captions descriptions, an evaluation of the language used by computational models to characterize people in images, a method that leverages eye-tracking information for the task of dependency parsing. Such richness of approaches and perspectives is in line with the purpose of the workshop, and confirms the growing interest for problems going beyond the task-specific integration of language and vision.",
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                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "The program of the workshop, besides 4 oral presentations and a session where papers published in the \"Findings of EMNLP 2020\" are presented, includes invited talks by Yonatan Bisk, Gemma Boleda, Angeliki Lazaridou, and Stefan Lee. The workshop received sponsorship by iDeaL SFB 1102. Best paper award is sponsored by HuggingFace. ",
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                "section": "Introduction",
                "sec_num": null
            },
            {
                "text": "LANTERN is a virtual event with four oral for the accepted long or short papers and also presentations from \"Findings of EMNLP 2020\". It also includes four invited presentations. The full schedule is presented below and all times are listed in CET.",
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                "section": "Conference Program",
                "sec_num": null
            },
            {
                "text": "14:00 ",
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                "section": "Conference Program",
                "sec_num": null
            }
        ],
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            "TABREF1": {
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                "text": "Eyes on the Parse: Using Gaze Features in Syntactic Parsing Abhishek Agrawal and Rudolf Rosa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Leveraging Visual Question Answering to Improve Text-to-Image Synthesis Stanislav Frolov, Shailza Jolly, J\u00f6rn Hees and Andreas Dengel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension Diana Galvan-Sosa, Jun Suzuki, Kyosuke Nishida, Koji Matsuda and Kentaro Inui . . . . . . . . . . . . 23 How Do Image Description Systems Describe People? A Targeted Assessment of System Competence in the PEOPLE-domain Emiel van Miltenburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 vii",
                "num": null
            }
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}