{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:BGUZ5V6CA76KMIVRNQXLVWOI5O","short_pith_number":"pith:BGUZ5V6C","canonical_record":{"source":{"id":"1710.11035","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-30T16:04:04Z","cross_cats_sorted":[],"title_canon_sha256":"42b47f43bf54208868eb3c56aae35803995d7f2563e3bd34d9b0401efdc013b1","abstract_canon_sha256":"5e2c902b0e45beea17e3480689d8203139293630b9af99203368dc8f5b4be405"},"schema_version":"1.0"},"canonical_sha256":"09a99ed7c207fca622b16c2ebad9c8ebbe83fdc57542887e5e785ac4adde1af0","source":{"kind":"arxiv","id":"1710.11035","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.11035","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"arxiv_version","alias_value":"1710.11035v2","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.11035","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"pith_short_12","alias_value":"BGUZ5V6CA76K","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BGUZ5V6CA76KMIVR","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BGUZ5V6C","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:BGUZ5V6CA76KMIVRNQXLVWOI5O","target":"record","payload":{"canonical_record":{"source":{"id":"1710.11035","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-30T16:04:04Z","cross_cats_sorted":[],"title_canon_sha256":"42b47f43bf54208868eb3c56aae35803995d7f2563e3bd34d9b0401efdc013b1","abstract_canon_sha256":"5e2c902b0e45beea17e3480689d8203139293630b9af99203368dc8f5b4be405"},"schema_version":"1.0"},"canonical_sha256":"09a99ed7c207fca622b16c2ebad9c8ebbe83fdc57542887e5e785ac4adde1af0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:22.253708Z","signature_b64":"N7d4vbCoy+gvmznP9Vuti0OSINnQNYAL7E6yfIxllDu6590cTcHdEUCZeY/lRDi7VtZkZU6kNY/Q3iy7WwuiAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09a99ed7c207fca622b16c2ebad9c8ebbe83fdc57542887e5e785ac4adde1af0","last_reissued_at":"2026-05-18T00:24:22.253206Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:22.253206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.11035","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TT8l7Gikxhr4AfdPHOon0P9q+s5nuL9covaGumoRFtC7qIiNT63IaHrT7fs5PdfkhrerLBntp9D4GzaoYgUTDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T13:18:59.769832Z"},"content_sha256":"c89d22c66b526ae9a6221a552fd80cf4dbe7d43edb28eabe071c60307901972c","schema_version":"1.0","event_id":"sha256:c89d22c66b526ae9a6221a552fd80cf4dbe7d43edb28eabe071c60307901972c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:BGUZ5V6CA76KMIVRNQXLVWOI5O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andrei Popescu-Belis, Claudiu Musat, Michael Baeriswyl, Pierre-Edouard Honnet","submitted_at":"2017-10-30T16:04:04Z","abstract_excerpt":"The goal of this work is to design a machine translation (MT) system for a low-resource family of dialects, collectively known as Swiss German, which are widely spoken in Switzerland but seldom written. We collected a significant number of parallel written resources to start with, up to a total of about 60k words. Moreover, we identified several other promising data sources for Swiss German. Then, we designed and compared three strategies for normalizing Swiss German input in order to address the regional diversity. We found that character-based neural MT was the best solution for text normali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.11035","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S5qq5AsrNhwuHXYryKGAZPF3Q7yOLBELho1oBrTCRxrPxbhL6BYrvnpiPBegdDGas+mGpD2ZAgX7A6KJCYQ6Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T13:18:59.770544Z"},"content_sha256":"104d879510053b615cc62fa6ae066b8d73053530a2ef3b73b8755874d4b089a0","schema_version":"1.0","event_id":"sha256:104d879510053b615cc62fa6ae066b8d73053530a2ef3b73b8755874d4b089a0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O/bundle.json","state_url":"https://pith.science/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T13:18:59Z","links":{"resolver":"https://pith.science/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O","bundle":"https://pith.science/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O/bundle.json","state":"https://pith.science/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BGUZ5V6CA76KMIVRNQXLVWOI5O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:BGUZ5V6CA76KMIVRNQXLVWOI5O","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5e2c902b0e45beea17e3480689d8203139293630b9af99203368dc8f5b4be405","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-30T16:04:04Z","title_canon_sha256":"42b47f43bf54208868eb3c56aae35803995d7f2563e3bd34d9b0401efdc013b1"},"schema_version":"1.0","source":{"id":"1710.11035","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.11035","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"arxiv_version","alias_value":"1710.11035v2","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.11035","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"pith_short_12","alias_value":"BGUZ5V6CA76K","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BGUZ5V6CA76KMIVR","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BGUZ5V6C","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:104d879510053b615cc62fa6ae066b8d73053530a2ef3b73b8755874d4b089a0","target":"graph","created_at":"2026-05-18T00:24:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The goal of this work is to design a machine translation (MT) system for a low-resource family of dialects, collectively known as Swiss German, which are widely spoken in Switzerland but seldom written. We collected a significant number of parallel written resources to start with, up to a total of about 60k words. Moreover, we identified several other promising data sources for Swiss German. Then, we designed and compared three strategies for normalizing Swiss German input in order to address the regional diversity. We found that character-based neural MT was the best solution for text normali","authors_text":"Andrei Popescu-Belis, Claudiu Musat, Michael Baeriswyl, Pierre-Edouard Honnet","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-30T16:04:04Z","title":"Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.11035","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c89d22c66b526ae9a6221a552fd80cf4dbe7d43edb28eabe071c60307901972c","target":"record","created_at":"2026-05-18T00:24:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5e2c902b0e45beea17e3480689d8203139293630b9af99203368dc8f5b4be405","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-30T16:04:04Z","title_canon_sha256":"42b47f43bf54208868eb3c56aae35803995d7f2563e3bd34d9b0401efdc013b1"},"schema_version":"1.0","source":{"id":"1710.11035","kind":"arxiv","version":2}},"canonical_sha256":"09a99ed7c207fca622b16c2ebad9c8ebbe83fdc57542887e5e785ac4adde1af0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"09a99ed7c207fca622b16c2ebad9c8ebbe83fdc57542887e5e785ac4adde1af0","first_computed_at":"2026-05-18T00:24:22.253206Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:22.253206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N7d4vbCoy+gvmznP9Vuti0OSINnQNYAL7E6yfIxllDu6590cTcHdEUCZeY/lRDi7VtZkZU6kNY/Q3iy7WwuiAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:22.253708Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.11035","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c89d22c66b526ae9a6221a552fd80cf4dbe7d43edb28eabe071c60307901972c","sha256:104d879510053b615cc62fa6ae066b8d73053530a2ef3b73b8755874d4b089a0"],"state_sha256":"4c8b239837e64a3025589d81493e0e12da10fdc9da2b27686bdff1dfae6251e4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uc3gfZ4Wkz2fYyNI/ugToGN2oA2beQY0rTIMaKg19l6GBdD331Lydmk5zWE7XyVlkhL7w9haiLu7Zuztf4xFCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T13:18:59.773929Z","bundle_sha256":"aad350a2b4b1fa3ddeb1a54700f320963f7c6322eab5f22981739c3c9b5c8460"}}