Towards High-Level Semantic Intelligence | Paper Citation Record

Observation c9dbf4e7-24ee-4d7a-aa83-4a5f291a8700 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Towards High-Level Semantic Intelligence Gradient-based learning applied to document recognition,

Reference 1

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Observation 277b4b36-ff9a-4673-bf91-e13216573f6a · outbound

This paper cites Maximum mutual information estimation of hidden markov model parameters for speech recognition,.

Towards High-Level Semantic Intelligence Maximum mutual information estimation of hidden markov model parameters for speech recognition,

Reference 2

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Observation 4f767e4f-d11b-4301-897d-15e23c3239c8 · outbound

This paper cites A neural proba- bilistic language model,.

Towards High-Level Semantic Intelligence A neural proba- bilistic language model,

Reference 3

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Observation f7a9f3f9-9800-40dc-973f-eebc42012e6b · outbound

This paper cites A mathematical theory of communication,.

Towards High-Level Semantic Intelligence A mathematical theory of communication,

Reference 4

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Observation b6daf291-9625-46af-9005-e940d9462a7b · outbound

This paper cites Recent contributions to the mathematical theory of com- munication,.

Towards High-Level Semantic Intelligence Recent contributions to the mathematical theory of com- munication,

Reference 5

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Observation 0d95b4fa-0c97-42b1-b299-bc974555fb1f · outbound

This paper cites Tomasello, m., constructing a language: a usage-based theory of language acquisition. cambridge, ma: Harvard university press, 2003. pp. 388. hardback, £29.95. isbn 0-674-01030-2.

Towards High-Level Semantic Intelligence Tomasello, m., constructing a language: a usage-based theory of language acquisition. cambridge, ma: Harvard university press, 2003. pp. 388. hardback, £29.95. isbn 0-674-01030-2

Reference 6

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Observation effa41a4-e5d6-468f-9b4b-46f6a0638b29 · outbound

This paper cites Bloom,How children learn the meanings of words.

Towards High-Level Semantic Intelligence Bloom,How children learn the meanings of words

Reference 7

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Observation 8fc23331-8827-4904-8f8a-812c777e832e · outbound

This paper cites Winner,The point of words: Children’s understanding of metaphor and irony.

Towards High-Level Semantic Intelligence Winner,The point of words: Children’s understanding of metaphor and irony

Reference 8

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Observation 0fe8661a-9d9e-4755-86ec-62e220344f13 · outbound

This paper cites Playing with expectations: A contextual view of humor development,.

Towards High-Level Semantic Intelligence Playing with expectations: A contextual view of humor development,

Reference 9

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Observation c4b2530b-16b1-4d61-a5c5-b60a8369c50b · outbound

This paper cites Empathy and moral development,.

Towards High-Level Semantic Intelligence Empathy and moral development,

Reference 10

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Observation 11579ff6-2d39-49e6-8109-560d894adea6 · outbound

This paper cites an unresolved cited work.

Towards High-Level Semantic Intelligence Unresolved cited work

Reference 11

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Observation e7dff02f-3df9-4946-a929-e2dc07cb18cb · outbound

This paper cites A theory of argumentative understand- ing: Relationships among position preference, judgments of goodness, memory and reasoning,.

Towards High-Level Semantic Intelligence A theory of argumentative understand- ing: Relationships among position preference, judgments of goodness, memory and reasoning,

Reference 12

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Observation 46138173-0843-4b41-b206-053c9a26c3aa · outbound

This paper cites Attention is all you need,.

Towards High-Level Semantic Intelligence Attention is all you need,

Reference 13

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Observation a904af26-84f6-4ccb-b50a-bedd73badcd5 · outbound

This paper cites GPT-4 Technical Report.

Towards High-Level Semantic Intelligence GPT-4 Technical Report

Reference 14

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Observation c4c9fdf6-7d02-48cc-95de-9444ddf7d195 · outbound

This paper cites an unresolved cited work.

Towards High-Level Semantic Intelligence Unresolved cited work

Reference 15

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Observation 00cad60f-9a13-48f7-b71e-a3f5db9cd6d9 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Towards High-Level Semantic Intelligence Imagenet classification with deep convolutional neural networks,

Reference 16

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Observation 3104b090-de81-49f1-8ca1-699f4617a492 · outbound

This paper cites Deep Speech: Scaling up end-to-end speech recognition.

Towards High-Level Semantic Intelligence Deep Speech: Scaling up end-to-end speech recognition

Reference 17

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Observation 928c392d-fe3f-40ac-81ec-997add166cd1 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

Towards High-Level Semantic Intelligence Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 18

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Observation 0cbb4655-294c-4f76-ab39-894da77598c2 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language understanding,.

Towards High-Level Semantic Intelligence Bert: Pre- training of deep bidirectional transformers for language understanding,

Reference 19

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Observation b4ba6c4b-d773-499f-9dd1-ab5c9518ae36 · outbound

This paper cites Improving language understanding by generative pre-training,.

Towards High-Level Semantic Intelligence Improving language understanding by generative pre-training,

Reference 20

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Observation 7bd113ba-c834-4249-ba97-5d22db8a511a · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Towards High-Level Semantic Intelligence Learning transferable visual models from natural language supervision,

Reference 21

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Observation b2732a47-decb-4477-9ef6-f9560f2bcbc2 · outbound

This paper cites Videobert: A joint model for video and language representation learning,.

Towards High-Level Semantic Intelligence Videobert: A joint model for video and language representation learning,

Reference 22

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Observation a3563a00-2af3-41db-83c8-78a612a85554 · outbound

This paper cites Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text,.

Towards High-Level Semantic Intelligence Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text,

Reference 23

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Observation a1ccc038-e140-4a5d-ba83-bdb289b0e9d7 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Towards High-Level Semantic Intelligence Flamingo: a visual language model for few-shot learning,

Reference 24

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Observation 227abffd-85bc-442c-af1a-56e11183fe7b · outbound

This paper cites Visual instruction tuning,.

Towards High-Level Semantic Intelligence Visual instruction tuning,

Reference 25

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Observation bdf2401d-0bae-4434-8723-ee804d79e213 · outbound

This paper cites Paper review:’sparks of artificial general intelligence: Early experiments with gpt-4’,.

Towards High-Level Semantic Intelligence Paper review:’sparks of artificial general intelligence: Early experiments with gpt-4’,

Reference 26

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Observation 9f58d57a-6b6f-48a1-a874-a0317d22e96b · outbound

This paper cites Open-Sora: Democratizing Efficient Video Production for All.

Towards High-Level Semantic Intelligence Open-Sora: Democratizing Efficient Video Production for All

Reference 27

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Observation d955b80b-f486-48e3-acf0-ca160cbe39d3 · outbound

This paper cites Seedance 2.0: Advancing Video Generation for World Complexity.

Towards High-Level Semantic Intelligence Seedance 2.0: Advancing Video Generation for World Complexity

Reference 28

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Observation 5fae12f0-f3d3-4963-b8b0-dd825f988f28 · outbound

This paper cites Large Language Models for Subjective Language Understanding: A Survey.

Towards High-Level Semantic Intelligence Large Language Models for Subjective Language Understanding: A Survey

Reference 29

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Observation c4d687b6-65c2-4a26-936a-71fc7a780dcc · outbound

This paper cites Looking beyond the obvious: A survey on abstract concept recognition for video understanding: Looking beyond the obvious: A survey on abstract concept.

Towards High-Level Semantic Intelligence Looking beyond the obvious: A survey on abstract concept recognition for video understanding: Looking beyond the obvious: A survey on abstract concept

Reference 30

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Observation 1307bb32-fcb1-4321-bedf-b4e72a97d1d0 · outbound

This paper cites Class-basedn-gram models of natural language,.

Towards High-Level Semantic Intelligence Class-basedn-gram models of natural language,

Reference 31

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Observation 79590d0a-8b98-4a74-a929-1ec99a1f9b16 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Towards High-Level Semantic Intelligence Efficient Estimation of Word Representations in Vector Space

Reference 32

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Observation a0d7cb64-58a4-45f8-b40a-dc16ebac3bc8 · outbound

This paper cites Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition,.

Towards High-Level Semantic Intelligence Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition,

Reference 33

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Observation 4a5235a3-f421-40bd-8ee3-fa095d196007 · outbound

This paper cites Message Understanding Conference- 6: A brief history,.

Towards High-Level Semantic Intelligence Message Understanding Conference- 6: A brief history,

Reference 34

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Observation 413c8121-df07-427d-9a08-3c82ff00fd51 · outbound

This paper cites Statistical phrase-based transla- tion,.

Towards High-Level Semantic Intelligence Statistical phrase-based transla- tion,

Reference 35

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Observation ee6d6a85-ba6b-40fb-ad70-1f43d5f64ccb · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Towards High-Level Semantic Intelligence Neural Machine Translation by Jointly Learning to Align and Translate

Reference 36

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Observation 6e419b96-0bbe-43cb-a51c-995757b8575d · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text,.

Towards High-Level Semantic Intelligence SQuAD: 100,000+ questions for machine comprehension of text,

Reference 37

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Observation 7e805a8f-c91f-44a4-be07-7280d3cb8fc0 · outbound

This paper cites A neural attention model for abstractive sentence summarization,.

Towards High-Level Semantic Intelligence A neural attention model for abstractive sentence summarization,

Reference 38

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Observation 3a319a71-fbed-4d7d-ac3a-42897f593d68 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases,.

Towards High-Level Semantic Intelligence Automatically constructing a corpus of sentential paraphrases,

Reference 39

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Observation 1f8aabab-8942-4c5d-a1d3-5512271d77dd · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Towards High-Level Semantic Intelligence Imagenet: A large-scale hierarchical image database,

Reference 40

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Observation bd1f18a0-d79b-46fc-9085-e07b62ce1e96 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks,.

Towards High-Level Semantic Intelligence Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 41

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Observation e6403cc5-991f-4b13-b5a3-60a210352a57 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Towards High-Level Semantic Intelligence Fully convolutional networks for semantic segmentation,

Reference 42

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Observation 2199ee92-9810-417d-94dc-4355ee9e969f · outbound

This paper cites Mask r-cnn,.

Towards High-Level Semantic Intelligence Mask r-cnn,

Reference 43

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Observation 62af743d-8d33-4ef1-9a5c-3678afa64d23 · outbound

This paper cites Realtime multi-person 2d pose estimation using part affinity fields,.

Towards High-Level Semantic Intelligence Realtime multi-person 2d pose estimation using part affinity fields,

Reference 44

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Observation 7b31c8c3-c5fe-4478-9499-e1625117d295 · outbound

This paper cites State of the art in example-based texture synthesis,.

Towards High-Level Semantic Intelligence State of the art in example-based texture synthesis,

Reference 45

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Observation dc3d3bc2-61fe-4e54-a453-587d0f4597f6 · outbound

This paper cites Texture synthesis using convo- lutional neural networks,.

Towards High-Level Semantic Intelligence Texture synthesis using convo- lutional neural networks,

Reference 46

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Observation e4ab2440-6e4a-48d2-b81f-99cf3cbdc149 · outbound

This paper cites Globally and locally consistent image completion,.

Towards High-Level Semantic Intelligence Globally and locally consistent image completion,

Reference 47

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Observation 64046091-f4c5-4bc0-93e5-6eb8697f53cb · outbound

This paper cites Image completion with structure propagation,.

Towards High-Level Semantic Intelligence Image completion with structure propagation,

Reference 48

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Observation 1bc2ebca-08c3-4d1b-8c98-6814baa382d1 · outbound

This paper cites Auto-Encoding Variational Bayes.

Towards High-Level Semantic Intelligence Auto-Encoding Variational Bayes

Reference 49

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Observation ad12e4e9-2258-44b0-8b71-5eea30f1ffbf · outbound

This paper cites Generative adversarial nets,.

Towards High-Level Semantic Intelligence Generative adversarial nets,

Reference 50

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Observation 9214cdf4-da90-460c-aa13-7af33848dd41 · outbound

This paper cites A tutorial on hidden markov models and selected applica- tions in speech recognition,.

Towards High-Level Semantic Intelligence A tutorial on hidden markov models and selected applica- tions in speech recognition,

Reference 51

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Observation 0c3b3de3-8f17-4a2a-b09b-462a206cf96f · outbound

This paper cites Suppression of acoustic noise in speech using spectral subtraction,.

Towards High-Level Semantic Intelligence Suppression of acoustic noise in speech using spectral subtraction,

Reference 52

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Observation c1028e37-d303-4665-a28c-d471d74a58d6 · outbound

This paper cites Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,.

Towards High-Level Semantic Intelligence Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:33.472024Z

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Observation f9409aec-605c-46f2-b336-d91929c4c27f · outbound

This paper cites X-vectors: Robust dnn embeddings for speaker recognition,.

Towards High-Level Semantic Intelligence X-vectors: Robust dnn embeddings for speaker recognition,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:33.536571Z

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Observation 2746ebbc-5dcb-4bfc-b447-f26f1fec4552 · outbound

This paper cites Emotional speech synthesis: a review.

Towards High-Level Semantic Intelligence Emotional speech synthesis: a review

Reference 55

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Observation c9cb962b-8cac-458b-9b25-f622f43f293d · outbound

This paper cites Speech parameter generation algorithms for hmm-based speech syn- thesis,.

Towards High-Level Semantic Intelligence Speech parameter generation algorithms for hmm-based speech syn- thesis,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:33.651302Z

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Observation 39f61dc7-5d07-475f-aa57-f2e649c54e3a · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Towards High-Level Semantic Intelligence WaveNet: A Generative Model for Raw Audio

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:33.716266Z

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Observation afeab639-2508-4ab8-a2d6-031aa1a64ec2 · outbound

This paper cites Tacotron: Towards End-to-End Speech Synthesis.

Towards High-Level Semantic Intelligence Tacotron: Towards End-to-End Speech Synthesis

Reference 58

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Observation baff9827-f56f-4b08-92e0-d6f2aab8b8de · outbound

This paper cites Imagebind: One embedding space to bind them all,.

Towards High-Level Semantic Intelligence Imagebind: One embedding space to bind them all,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:33.880024Z

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Observation 919a19ec-a234-4373-9f59-d69b6a68a314 · outbound

This paper cites Making computers laugh: Inves- tigations in automatic humor recognition,.

Towards High-Level Semantic Intelligence Making computers laugh: Inves- tigations in automatic humor recognition,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:33.966317Z

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Observation 159c79ca-cace-44e9-89bd-253bd688004e · outbound

This paper cites SemEval-2017 task 6: #HashtagWars: Learning a sense of humor,.

Towards High-Level Semantic Intelligence SemEval-2017 task 6: #HashtagWars: Learning a sense of humor,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:34.053367Z

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Observation 68b9667d-5af5-47ff-b3a7-e7d891538bfe · outbound

This paper cites “president vows to cut <taxes>hair.

Towards High-Level Semantic Intelligence “president vows to cut <taxes>hair

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:34.115433Z

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Observation 305266ba-c71b-4ebb-87dd-c4ffa7e17788 · outbound

This paper cites Cards against AI: Predicting humor in a fill-in-the-blank party game,.

Towards High-Level Semantic Intelligence Cards against AI: Predicting humor in a fill-in-the-blank party game,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:34.174724Z

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Observation b71c99e6-d06e-453e-8087-9d3b124a3b12 · outbound

This paper cites Can language models make fun? a case study in Chinese comical crosstalk,.

Towards High-Level Semantic Intelligence Can language models make fun? a case study in Chinese comical crosstalk,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:34.249972Z

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Observation 35c7fe66-76c9-4fe4-bddd-c1e67757d913 · outbound

This paper cites Talk funny! a large-scale humor response dataset with chain-of-humor interpretation,.

Towards High-Level Semantic Intelligence Talk funny! a large-scale humor response dataset with chain-of-humor interpretation,

Reference 65

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Observation 50b18314-261b-4e85-b334-d47d3322c0e2 · outbound

This paper cites Chumor 2.0: Towards better benchmarking Chinese humor understanding from (ruo zhi ba),.

Towards High-Level Semantic Intelligence Chumor 2.0: Towards better benchmarking Chinese humor understanding from (ruo zhi ba),

Reference 66

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Observation e8d851df-84cd-419c-85b4-c756a9520918 · outbound

This paper cites “what do you call a dog that is incontrovertibly true? dogma.

Towards High-Level Semantic Intelligence “what do you call a dog that is incontrovertibly true? dogma

Reference 67

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Observation 610de6d9-82ef-440d-9e8f-d58673d5321a · outbound

This paper cites CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processing.

Towards High-Level Semantic Intelligence CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processing

Reference 68

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Observation bd47d40d-06ba-4423-9d20-a7586587ab00 · outbound

This paper cites Comparing apples to oranges: A dataset & analysis of LLM humour understanding from traditional puns to topical jokes,.

Towards High-Level Semantic Intelligence Comparing apples to oranges: A dataset & analysis of LLM humour understanding from traditional puns to topical jokes,

Reference 69

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Observation 78597be5-6c6c-4f82-b82f-9a115fe42f2d · outbound

This paper cites Drivel-ology: Challenging llms with interpreting nonsense with depth,.

Towards High-Level Semantic Intelligence Drivel-ology: Challenging llms with interpreting nonsense with depth,

Reference 70

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Observation 52aa0cf0-0349-421f-9b61-637bea711db0 · outbound

This paper cites Do androids laugh at electric sheep? humor “understanding.

Towards High-Level Semantic Intelligence Do androids laugh at electric sheep? humor “understanding

Reference 71

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Observation 808e1485-99cd-4601-9c71-21dcc37af998 · outbound

This paper cites Humor in ai: Massive scale crowd-sourced preferences and benchmarks for cartoon caption- ing,.

Towards High-Level Semantic Intelligence Humor in ai: Massive scale crowd-sourced preferences and benchmarks for cartoon caption- ing,

Reference 72

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Observation acdd9115-4cbf-4e90-9112-a6db2828ffc3 · outbound

This paper cites Let’s think outside the box: Exploring leap-of-thought in large language models with creative humor generation,.

Towards High-Level Semantic Intelligence Let’s think outside the box: Exploring leap-of-thought in large language models with creative humor generation,

Reference 73

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Observation 8f675ebd-bacf-4076-a88b-b3e7fc2d41aa · outbound

This paper cites Cracking the code of juxtaposition: can ai models understand the humorous contradictions,.

Towards High-Level Semantic Intelligence Cracking the code of juxtaposition: can ai models understand the humorous contradictions,

Reference 74

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Observation caad345d-e85e-43e2-8898-70996b93f245 · outbound

This paper cites Visionarena: 230k real world user-vlm conversations with preference labels,.

Towards High-Level Semantic Intelligence Visionarena: 230k real world user-vlm conversations with preference labels,

Reference 75

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Observation bdaa9010-a4a9-4ab6-98c1-98bdc1f8a664 · outbound

This paper cites D-humor: Dark humor understanding via multimodal open-ended reasoning,.

Towards High-Level Semantic Intelligence D-humor: Dark humor understanding via multimodal open-ended reasoning,

Reference 76

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Observation 2614ce19-b2fc-45e1-82d1-f9f2fea1f840 · outbound

This paper cites Humor in pixels: Benchmarking large multimodal models understanding of online comics,.

Towards High-Level Semantic Intelligence Humor in pixels: Benchmarking large multimodal models understanding of online comics,

Reference 77

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Observation 39fb7113-e84b-437c-8b09-102e6571cd02 · outbound

This paper cites HumorDB: Can AI understand graphical humor?.

Towards High-Level Semantic Intelligence HumorDB: Can AI understand graphical humor?

Reference 78

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Observation 90d46e73-f65f-4287-9a63-b1f806f9562b · outbound

This paper cites v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound.

Towards High-Level Semantic Intelligence v-HUB: A Benchmark for Video Humor Understanding from Vision and Sound

Reference 79

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Observation 35b70e3b-3beb-41e7-a262-b9ccc3704dbe · outbound

This paper cites GODBench: A benchmark for multimodal large language models in video comment art,.

Towards High-Level Semantic Intelligence GODBench: A benchmark for multimodal large language models in video comment art,

Reference 80

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Observation 661b1361-e493-4d14-a56e-de3aeae4dd1e · outbound

This paper cites Can language models laugh at YouTube short-form videos?.

Towards High-Level Semantic Intelligence Can language models laugh at YouTube short-form videos?

Reference 81

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Observation 8d6e5538-ea86-4691-95f7-cd87aa6fd35c · outbound

This paper cites Towards multimodal prediction of spontaneous humor: A novel dataset and first results,.

Towards High-Level Semantic Intelligence Towards multimodal prediction of spontaneous humor: A novel dataset and first results,

Reference 82

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Observation 60bb9a9a-63cb-4c1b-8848-c609f3b83b68 · outbound

This paper cites StandUp4AI: A new multilingual dataset for humor detection in stand- up comedy videos,.

Towards High-Level Semantic Intelligence StandUp4AI: A new multilingual dataset for humor detection in stand- up comedy videos,

Reference 83

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Observation 488a3b9f-d733-4bb7-a581-58ab3c50c60c · outbound

This paper cites Testing the ability of language models to interpret figurative language,.

Towards High-Level Semantic Intelligence Testing the ability of language models to interpret figurative language,

Reference 84

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Observation 10ca347e-1071-400c-a02d-55e412870542 · outbound

This paper cites FLUTE: Figurative language understanding through textual explanations,.

Towards High-Level Semantic Intelligence FLUTE: Figurative language understanding through textual explanations,

Reference 85

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Observation c5527058-faf0-453d-adef-df0a931313f6 · outbound

This paper cites Chinese metaphorical relation extraction,.

Towards High-Level Semantic Intelligence Chinese metaphorical relation extraction,

Reference 86

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Observation 767c08dd-8744-4c23-92d4-e7bb3f96e13f · outbound

This paper cites Chinese idiom paraphrasing,.

Towards High-Level Semantic Intelligence Chinese idiom paraphrasing,

Reference 87

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Observation 31c096cf-b288-4e21-984f-162548a1cd0a · outbound

This paper cites Multi-lingual and multi- cultural figurative language understanding,.

Towards High-Level Semantic Intelligence Multi-lingual and multi- cultural figurative language understanding,

Reference 88

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Observation dd8dc4c2-bda0-472d-becf-2c92d0e02bca · outbound

This paper cites NewsMet : A ‘do it all’ dataset of contemporary metaphors in news headlines,.

Towards High-Level Semantic Intelligence NewsMet : A ‘do it all’ dataset of contemporary metaphors in news headlines,

Reference 89

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Observation d1ce41c0-b2d7-4b92-814b-d2c9767bf286 · outbound

This paper cites MMTE: Corpus and metrics for evaluating machine translation quality of metaphorical language,.

Towards High-Level Semantic Intelligence MMTE: Corpus and metrics for evaluating machine translation quality of metaphorical language,

Reference 90

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Observation 58903f20-7d71-4966-b280-ccc1524cbc57 · outbound

This paper cites MetaPro 2.0: Computational metaphor processing on the effectiveness of anomalous language modeling,.

Towards High-Level Semantic Intelligence MetaPro 2.0: Computational metaphor processing on the effectiveness of anomalous language modeling,

Reference 91

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Observation e523dea2-1dad-4a6f-a847-97bc00785440 · outbound

This paper cites Metaphor understanding challenge dataset for LLMs,.

Towards High-Level Semantic Intelligence Metaphor understanding challenge dataset for LLMs,

Reference 92

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Observation 82947df9-a48b-4f7a-b6df-c359868ed67e · outbound

This paper cites Enhancing information extraction with metorie: A metaphor and trap-based dataset for cross-domain fine-tuning,.

Towards High-Level Semantic Intelligence Enhancing information extraction with metorie: A metaphor and trap-based dataset for cross-domain fine-tuning,

Reference 93

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Observation b00ddf23-56c0-4b48-8257-32ccf097a020 · outbound

This paper cites Investigating the impact of conceptual metaphors on llm-based nli through shapley interactions,.

Towards High-Level Semantic Intelligence Investigating the impact of conceptual metaphors on llm-based nli through shapley interactions,

Reference 94

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Observation 8e7146e6-0c20-4716-af17-aa04076eccc1 · outbound

This paper cites Automatic extraction of metaphoric analogies from literary texts: Task formulation, dataset construction, and evaluation,.

Towards High-Level Semantic Intelligence Automatic extraction of metaphoric analogies from literary texts: Task formulation, dataset construction, and evaluation,

Reference 95

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Observation e46fe196-905c-406c-bd8e-1f851a995d19 · outbound

This paper cites Comparative study of multilingual idioms and similes in large language models,.

Towards High-Level Semantic Intelligence Comparative study of multilingual idioms and similes in large language models,

Reference 96

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Observation 2d5cc939-e7d2-4469-8325-57a9e0780db6 · outbound

This paper cites I spy a metaphor: Large language models and diffusion models co-create visual metaphors,.

Towards High-Level Semantic Intelligence I spy a metaphor: Large language models and diffusion models co-create visual metaphors,

Reference 97

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Observation d83e82dd-9bc4-457e-b527-0ace628af36b · outbound

This paper cites MultiCMET: A novel Chinese benchmark for understanding multimodal metaphor,.

Towards High-Level Semantic Intelligence MultiCMET: A novel Chinese benchmark for understanding multimodal metaphor,

Reference 98

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Observation a96ea23c-32a3-406d-ade1-c4192f11c38d · outbound

This paper cites MemeCap: A dataset for captioning and interpreting memes,.

Towards High-Level Semantic Intelligence MemeCap: A dataset for captioning and interpreting memes,

Reference 99

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Observation 1e8cbc95-83f4-4227-ba80-88dc0eba9cf8 · outbound

This paper cites Cultural bias matters: A cross-cultural benchmark dataset and sentiment-enriched model for understanding multimodal metaphors,.

Towards High-Level Semantic Intelligence Cultural bias matters: A cross-cultural benchmark dataset and sentiment-enriched model for understanding multimodal metaphors,

Reference 100

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