We present the first comprehensive set of guidelines for German Abstract Meaning Representation (Deutsche AMR, DeAMR) along with an annotated corpus of 400 DeAMR. Taking English AMR (EnAMR) as our starting point, we propose significant adaptations to faithfully represent the structure and semantics of German, focusing particularly on verb frames, compound words, and modality. We validate our annotation through inter-annotator agreement and further evaluate our corpus with a comparison of structural divergences between EnAMR and DeAMR on parallel sentences, replicating previous work that finds both cases of cross-lingual structural alignment and cases of meaningful linguistic divergence. Finally, we fine-tune state-of-the-art multi-lingual and cross-lingual AMR parsers on our corpus and find that, while our small corpus is insufficient to produce quality output, there is a need to continue develop and evaluate against gold non-English AMR data.

Discourse Representation Theory (DRT) distinguishes itself from other semantic representation frameworks by its ability to model complex semantic and discourse phenomena through structural nesting and variable binding.While seq2seq models hold the state of the art on DRT parsing, their accuracy degrades with the complexity of the sentence, and they sometimes struggle to produce well-formed DRT representations.We introduce the AMS parser, a compositional, neurosymbolic semantic parser for DRT.It rests on a novel mechanism for predicting quantifier scope.We show that the AMS parser reliably produces well-formed outputs and performs well on DRT parsing, especially on complex sentences.1

Message from the General Chair I am happy to welcome you to EMNLP-2023 in Singapore!Like EMNLP-2021, EMNLP-2022, and other ACL-related meetings, we decided to host EMNLP-2023 as another hybrid conference having both in-person and virtual presentations and participants.We are not sure how long this style of our meetings will last.However, we have already accustomed to this style of conferences, which has its own advantages, while it causes a heavy burden to those organizing such events.The past one year has been a terrific and thrilling year since the advent of ChatGPT and other Large Language Models.Any people having access to those models has posed a big impact on people's impression about AI and has started to give them a feeling of fear.People now can do not only natural conversation with AI but also conduct various natural language tasks using our own languages.We now know it is difficult to guarantee that Large Language Models produce honest and harmless outputs.We have found that good prompting, demonstrations and complex ones like the Chain of Thought prompting draw out or enhance the emergent abilities of Large Language Models.However, we still don't know precisely why and how such in-context learning works.This year's EMNLP highlights a theme track, "Large Language Models and the Future of NLP."I hope we can see enthusiastic discussions and innovative ideas will be presented in EMNLP-2023.One big trial is that the Program Chairs decided to use OpenReview as the cradle of the main conference papers, for making reviews and author responses publicly available.The motivation and effects of this trial will be explained by the PC Chairs.Another important trial is to rent out the Universal Studio Singapore for our Social Event.I hope everyone will enjoy this event.EMNLP-2023 is the biggest conference ever in the SIGDAT history.Organizing such a big event is very difficult.As the General Chair, the most important and difficult task is to organize all the committees by a group of enthusiastic and talented people.I was very fortunate to be able to collect great committee members.Without such a wonderful group of colleagues, it almost has been impossible to make this great event happen.I would like to send my sincere thanks to all the members of our organization teams.Here, I only list the chairs by names, but I also like to send gratitude from my heart to all the people involved in EMNLP-2023, including keynote speakers, panelists, workshop organizers, tutorial tutors, senior area chairs, area chairs, reviewers, volunteers, sponsors, the Underline team, and all of you attending EMNLP-2023 in-person or virtually.• The program chairs -Houda Bouamor, Juan Pino, and Kalika Bali -who made a number of innovations and handled a huge number of submitted papers.I cannot help but be grateful for their tireless work.• The Local Chair and the Local Team -Haizhou Li the Chair organized and lead a wonderful group of people.While I cannot name every one of them, weekly meetings with the team members including related Chairs made our communication smooth and worked as a good time-keeper.For the remaining committee chairs, I only list them by names, as I cannot give all my gratitude only with short messages.

The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure.MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing.Here, we ask the question of which design differences between MRs are meaningful and semantically-rooted, and which are superficial.We present a methodology for normalizing discrepancies between MRs at the compositional level (Lindemann et al., 2019), finding that we can normalize the majority of divergent phenomena using linguistically-grounded rules.Our work significantly increases the match in compositional structure between MRs and improves multi-task learning (MTL) in a low-resource setting, serving as a proof of concept for future broad-scale cross-MR normalization.

AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.

Most semantic parsers that map sentences to graph-based meaning representations are handdesigned for specific graphbanks.We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks.Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.

Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, Alexander Koller. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Parsing for a wide variety of grammar formalisms can be performed by intersecting finite tree automata.However, naive implementations of parsing by intersection are very inefficient.We present techniques that speed up tree-automata-based parsing, to the point that it becomes practically feasible on realistic data when applied to context-free, TAG, and graph parsing.For graph parsing, we obtain the best runtimes in the literature.

Jonas Groschwitz, Alexander Koller, Christoph Teichmann. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.