Disambiguate Shengjie Li (closes #5217) (#5778)

Author: mjpost

data/xml/2020.acl.xml

@@ -7850,7 +7850,7 @@
       <title>Cross-modal Coherence Modeling for Caption Generation</title>
       <author><first>Malihe</first><last>Alikhani</last></author>
       <author><first>Piyush</first><last>Sharma</last></author>
-      <author><first>Shengjie</first><last>Li</last></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last></author>
       <author><first>Radu</first><last>Soricut</last></author>
       <author><first>Matthew</first><last>Stone</last></author>
       <pages>6525–6535</pages>

data/xml/2021.codi.xml

@@ -252,7 +252,7 @@
     <paper id="2">
       <title>Neural Anaphora Resolution in Dialogue</title>
       <author><first>Hideo</first><last>Kobayashi</last></author>
-      <author><first>Shengjie</first><last>Li</last></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last></author>
       <author><first>Vincent</first><last>Ng</last></author>
       <pages>16–31</pages>
       <abstract>We describe the systems that we developed for the three tracks of the CODI-CRAC 2021 shared task, namely entity coreference resolution, bridging resolution, and discourse deixis resolution. Our team ranked second for entity coreference resolution, first for bridging resolution, and first for discourse deixis resolution.</abstract>
@@ -324,7 +324,7 @@
     </paper>
     <paper id="8">
       <title>The <fixed-case>CODI</fixed-case>-<fixed-case>CRAC</fixed-case> 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis Resolution in Dialogue: A Cross-Team Analysis</title>
-      <author><first>Shengjie</first><last>Li</last></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last></author>
       <author><first>Hideo</first><last>Kobayashi</last></author>
       <author><first>Vincent</first><last>Ng</last></author>
       <pages>71–95</pages>

data/xml/2022.codi.xml

@@ -212,7 +212,7 @@
     </paper>
     <paper id="4">
       <title>Neural Anaphora Resolution in Dialogue Revisited</title>
-      <author><first>Shengjie</first><last>Li</last></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last></author>
       <author><first>Hideo</first><last>Kobayashi</last></author>
       <author><first>Vincent</first><last>Ng</last></author>
       <pages>32–47</pages>

data/xml/2022.coling.xml

@@ -6093,7 +6093,7 @@
       <author><first>Junpeng</first><last>Liu</last></author>
       <author><first>Yanyan</first><last>Zou</last></author>
       <author><first>Yuxuan</first><last>Xi</last></author>
-      <author><first>Shengjie</first><last>Li</last></author>
+      <author id="shengjie-li-peking"><first>Shengjie</first><last>Li</last></author>
       <author><first>Mian</first><last>Ma</last></author>
       <author><first>Zhuoye</first><last>Ding</last></author>
       <pages>6050–6056</pages>

data/xml/2022.emnlp.xml

@@ -10622,7 +10622,7 @@
     </paper>
     <paper id="778">
       <title>End-to-End Neural Discourse Deixis Resolution in Dialogue</title>
-      <author><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
       <author><first>Vincent</first><last>Ng</last><affiliation>University of Texas at Dallas</affiliation></author>
       <pages>11322-11334</pages>
       <abstract>We adapt Lee et al.’s (2018) span-based entity coreference model to the task of end-to-end discourse deixis resolution in dialogue, specifically by proposing extensions to their model that exploit task-specific characteristics. The resulting model, dd-utt, achieves state-of-the-art results on the four datasets in the CODI-CRAC 2021 shared task.</abstract>

data/xml/2024.acl.xml

@@ -5745,7 +5745,7 @@
     </paper>
     <paper id="414">
       <title>Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art</title>
-      <author><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
       <author><first>Vincent</first><last>Ng</last><affiliation>University of Texas at Dallas</affiliation></author>
       <pages>7661-7681</pages>
       <abstract>Cross-prompt automated essay scoring (AES), an under-investigated but challenging task that has gained increasing popularity in the AES community, aims to train an AES system that can generalize well to prompts that are unseen during model training. While recently-developed cross-prompt AES models have combined essay representations that are learned via sophisticated neural architectures with so-called prompt-independent features, an intriguing question is: are complex neural models needed to achieve state-of-the-art results? We answer this question by abandoning sophisticated neural architectures and developing a purely feature-based approach to cross-prompt AES that adopts a simple neural architecture. Experiments on the ASAP dataset demonstrate that our simple approach to cross-prompt AES can achieve state-of-the-art results.</abstract>

data/xml/2024.emnlp.xml

@@ -13754,7 +13754,7 @@
     </paper>
     <paper id="991">
       <title>Automated Essay Scoring: A Reflection on the State of the Art</title>
-      <author><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
       <author><first>Vincent</first><last>Ng</last><affiliation>University of Texas at Dallas</affiliation></author>
       <pages>17876-17888</pages>
       <abstract>While steady progress has been made on the task of automated essay scoring (AES) in the past decade, much of the recent work in this area has focused on developing models that beat existing models on a standard evaluation dataset. While improving performance numbers remains an important goal in the short term, such a focus is not necessarily beneficial for the long-term development of the field. We reflect on the state of the art in AES research, discussing issues that we believe can encourage researchers to think bigger than improving performance numbers with the ultimate goal of triggering discussion among AES researchers on how we should move forward.</abstract>

data/xml/2024.naacl.xml

@@ -6658,7 +6658,7 @@
     </paper>
     <paper id="468">
       <title><fixed-case>ICLE</fixed-case>++: Modeling Fine-Grained Traits for Holistic Essay Scoring</title>
-      <author><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
+      <author id="shengjie-li"><first>Shengjie</first><last>Li</last><affiliation>University of Texas at Dallas</affiliation></author>
       <author><first>Vincent</first><last>Ng</last><affiliation>University of Texas at Dallas, Central China Normal University and State University of New York at Stony Brook</affiliation></author>
       <pages>8465-8486</pages>
       <abstract>The majority of the recently developed models for automated essay scoring (AES) are evaluated solely on the ASAP corpus. However, ASAP is not without its limitations. For instance, it is not clear whether models trained on ASAP can generalize well when evaluated on other corpora. In light of these limitations, we introduce ICLE++, a corpus of persuasive student essays annotated with both holistic scores and trait-specific scores. Not only can ICLE++ be used to test the generalizability of AES models trained on ASAP, but it can also facilitate the evaluation of models developed for newer AES problems such as multi-trait scoring and cross-prompt scoring. We believe that ICLE++, which represents a culmination of our long-term effort in annotating the essays in the ICLE corpus, contributes to the set of much-needed annotated corpora for AES research.</abstract>

data/yaml/name_variants.yaml

@@ -10944,3 +10944,11 @@
 - canonical: {first: Chong, last: Zhang}
   comment: May refer to multiple people
   id: chong-zhang
+- canonical: {first: Shengjie, last: Li}
+  comment: University of Texas at Dallas
+  id: shengjie-li
+  orcid: 0000-0002-5442-5464
+- canonical: {first: Shengjie, last: Li}
+  id: shengjie-li-peking
+  comment: Peking University
+  orcid: 0000-0003-3489-9125